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create mode 100644 docs/tutorials/soar_tutorial/Images/08/image2.png create mode 100644 docs/tutorials/soar_tutorial/Images/08/image3.png diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md index 5187c301..1a52f372 100644 --- a/docs/tutorials/index.md +++ b/docs/tutorials/index.md @@ -1,5 +1,5 @@ # Tutorials -- [Official Soar Tutorial](soar_tutorial/index.md) -- [Introduction to the Soar Debugger](IntroSoarDebugger.md) -- [Soar Markup Language: Quick Start Guide](SMLQuickStartGuide.md) \ No newline at end of file +- [Official Soar Tutorial](soar_tutorial/index.md) +- [Introduction to the Soar Debugger](IntroSoarDebugger.md) +- [Soar Markup Language: Quick Start Guide](SMLQuickStartGuide.md) diff --git a/docs/tutorials/soar_tutorial/01.md b/docs/tutorials/soar_tutorial/01.md new file mode 100644 index 00000000..b94bb258 --- /dev/null +++ b/docs/tutorials/soar_tutorial/01.md @@ -0,0 +1,2145 @@ +# Part I: Simple Soar Programs + +Throughout this tutorial, you will be writing and running Soar programs +for simple tasks. In this part, you will be writing simple programs that +do not use Soar subgoaling or chunking mechanisms. + +## Installing Soar + +Before reading further in the tutorial, you should install Soar and its +associated software on your computer so that you can run the examples +and exercises. Using your favorite browser, visit the Soar tutorial +downloads page which you can find off the following link: +. Follow the +directions on that page for installation. This will allow you to install +three downloads that include with the most recent release of Soar, the +Soar Debugger, the Eaters game, the TankSoar game, and VisualSoar. + +## Building a Simple Soar Agent Using Rules + +All of the knowledge in a Soar agent is represented as if-then _rules_. +In Soar, rules are called _productions,_ and we will use the terms +interchangeably. Rules are used to select and apply things called +_operators,_ and much of this tutorial will be describing how rules and +operators fit together. But before we get to operators, we are going to +learn about writing rules for a very simple agent – one that just prints +“Hello World.” Before we dive into rules, you will need to start up Soar +so that we can run the programs you write. + +### Creating a Soar Agent and Using the Soar Debugger + +To start Soar, double click (or whatever is appropriate on your +platform) on the Soar Debugger script in the folder where you installed +Soar. After Soar has started, you will have a new window on your screen. +This window is the Soar Debugger, which is shown below. This program +will be used to run all of your Soar programs. Your version may look +somewhat different, depending on the features added since this document +was written. + +![](Images/01/image1.png) + +The large window to the left is called the Interaction window (above). +During Soar execution, all debugging information and print statements +will be displayed in the Interaction window. Just below the interaction +window is the Command Box for typing in user commands. The windows on +the right display information that you will find useful later. + +When the Soar Debugger starts, a default agent entitled ‘soar1’ is +created. The name of the agent can be seen on the title bar. The bottom +of the window contains buttons for single stepping, running, or stopping +an agent. When it is time to run your Soar programs, commands can either +be entered by typing them into the command box, or by clicking the +buttons + +![](Images/01/image2.png) + +### Hello-World Rule: English + +The first Soar agent we will examine prints “Hello World”. In general, +an operator should perform this type of activity, but in order to ease +you slowly into Soar, we start with a single rule. Below is a simple +rule written in English. + +```Soar +hello-world: +If I exist, then write “Hello World” and halt. +``` + +Soar cannot directly interpret English, so you must write rules in +Soar’s very stylized and precise language. A major component of the +rest of this tutorial is teaching you that language. + +Soar works by testing the ”if” parts of rules (“If I exist”). These ”if” +parts are called _conditions_. If all of the conditions of a rule are +true in the current situation, the ”then” parts, or _actions,_ of the +rule (“write ‘Hello World’ and halt”) are executed, which usually +involves making changes to _working memory,_ which holds all of the +dynamic data structures in a Soar program. Executing the actions of a +rule is called _firing_ the rule. To determine if the conditions are +true, Soar compares them to data structures in working memory. Working +memory defines the current situation, which for an agent consists of its +perception of its world, results of intermediate calculations, active +goals, and operators. + +The figure below shows a high level view of Soar. On the right are all +of the underlying architectural processes that select operators, create +states, learn new rules, etc. These are all written in C and you will +not be changing them at all (so don’t worry if you don’t know C). In the +upper center is the rule memory, which contains all of the rules. Rules +encode the long-term knowledge in Soar and adding them is the way Soar +is programmed – you do not change the way Soar creates states or learns, +but you create rules. Working memory is where short-term information is +stored and your rules will create and test structures in working memory. +In contrast to C or Java, which have programs that are executed +sequentially, Soar has rules that are matched and executed in parallel. +C and Java have data structures, variables, etc. while Soar has working +memory. While these other languages have a combination of local and +global data, Soar has no local data – everything in working memory is +global. Working memory consists of a graph structure of individual +_elements_. The elements are created by rules as well as from sensors +(not shown in the diagram), and the Soar architecture creates a limited +set of working memory elements (the core part of states) automatically. +Working memory is matched against rule memory to determine which rules +will fire. When rules fire, they can make changes to working memory, as +well as performing simple actions such as printing messages in the +interaction window. + +### Loading and Running Hello-World + +Before going through the nitty-gritty of Soar syntax, let’s see an +example of Soar executing the hello-world rule. In order to execute the +hello-world rule, it must be loaded from a file into Soar’s rule memory. +You can load in the rules from a file via the Interaction window by +either clicking the “source” button or by using the File menu at the top +left (you can also type the source command directly into the interaction +window, but for that you need to know the path to the file). + +To use the File menu, click on the toolbar as shown in the figure below. + +![](Images/01/image3.png) + +The following menu will pop up; select Load source file… + +![](Images/01/image4.png) + +Alternatively, click on the “source” button at the bottom of the +interaction window. In both cases, a menu to select the file will pop +up, such as below (your menu might have a different set of files +available). You can navigate in this menu to get to the correct +directory (`/Agents/`) and then select the file you want to +load (hello-world-rule.soar). + +![](Images/01/image5.png) + +When rules are loaded into Soar, an “\*” is printed for each rule. When +the file has loaded, you will see the word “source” followed by the +location of the file in the interaction window, + +At this point you can type run, or click on the run button in the bottom +left of the interaction window. The first thing that happens is that +Soar automatically creates the initial state, S1 (but this is not shown +in the interaction window, but is shown in the window on the right where +the state S1 is printed). All of working memory is organized as states, +so even before any rules fire, a state is created. After the state is +created the rule matches (details explained later) and it fires, +performing its actions, printing “Hello World” and then halting. + +![](Images/01/image6.png) + +## Hello-World Rule: Soar + +As stated earlier, Soar has a stylized and precise language for writing +rules. Here is the Soar rule for hello-world. + +```Soar +sp {hello-world +(state ^type state) +--> +(write |Hello World|) +(halt)} +``` + +Every rule starts with the symbol “sp”, which stands for “Soar +production.” The remainder of the rule body is enclosed in curly braces: +“{“ and “}”. The body consists of the rule name, followed by one or more +conditions (the “if” part), then the symbol “-->“, and then one or more +actions (the “then” part). Below is a template for rules: + +```Soar +sp {rule\*name +(condition) +(condition) + +… +--> +(action) +(action) + +…} +``` + +The name of the rule can be any arbitrary combination of letters, +numbers, dashes (“-“), and asterisks (“\*”). The one exception is that a +name cannot be a single letter followed by a number such as S1 or O45. +Soar reserves those names for its own purposes. The specific name is +irrelevant to the operation of the rule, but you should always pick a +name that is meaningful. Following the name, there must be at least one +condition. A condition tests for the existence (or absence) of data in +working memory. If all of the conditions match, then the rule will fire +and all of the actions are performed. Most actions create new working +memory elements or remove existing working memory elements. Some actions +create preferences for selecting operators (more on that later), while +others perform functions such as writing text to the screen or halting +as in hello-world. + +In the next several sections we will explore the structure of conditions +and actions and explain what all of the special symbols, such as `` +and ^type, mean. Before doing that, we need to first look at the +structure of working memory in more detail. + +### Working Memory + +Working memory contains all of a Soar agent’s dynamic information about +its world and its internal reasoning. It contains sensor data, +intermediate calculations, current operators and goals. In Soar, all of +working memory is organized as graph structures in _states_. Thus, every +working memory element is connected directly or indirectly to a state +symbol. For the first agents you will build, there will be a single +state. Below is a simple example of what the structure of working memory +might be like if the agent were representing that there were two blocks, +one on top of the other, which is then on a table as shown in the figure +below. + +Below is a graphic picture of the working memory structures that Soar +creates automatically for every agent – so although not shown in the +above figure, S1 would have these additional structures. + +As a graph, there are nodes (or vertices) and links (or edges). The +nodes, such as S1, B1, B2, A, and blue, are connected by links, such as +block, table, name, color, and superstate. Soar has two kinds of nodes: +_identifiers_ and _constants._ The nodes that can have links emanating +from them (they are non-terminal nodes), such as S1 and B1, are called +_identifiers, while the others (they are terminal nodes), such as state, +blue, block, and nil, are called constants_. + +In the example above, S1 is the identifier for the state. All of the +identifiers symbols are created automatically by Soar and consist of a +single letter followed by a number. Although I2 and I3 in the lower +figure do not have any links emanating from them, they are identifiers +and can have additional substructures added later. In contrast, the +symbol state is not an identifier and cannot have links emanating from +it. The links are called _attributes_ in Soar and are prefaced by a “^”. +Only identifiers have attributes. In the bottom figure, S1 has three +attributes: superstate, io, and type. I1 has two: output-link and +input-link. + +Working memory is actually built of individual elements which are +triples of an identifier, an attribute, and _value,_ and where the value +is that node pointed to by the attribute. The value can be either a +constant or an identifier. In the example above, the symbols nil, I1, +state, I2, and I3 are all _values_. It is these triples that you add and +delete when writing Soar programs. + +There are five working memory elements in the figure above: + +```Soar +S1 ^superstate nil +S1 ^io I1 +S1 ^type state +I1 ^output-link I2 +I1 ^input-link I3 +``` + +This is the minimal contents of working memory, and as your programs get +larger and more complex, they will manipulate many more working memory +elements. + +A collection of working memory elements that share the same first +identifier is called an _object_. For example, the three working memory +elements that have S1 as their identifier are all part of the state +object. The working memory elements that make up an object are called +_augmentations_. Although we call them objects, they consist of the +individual working memory elements that share the same identifier. +Objects are usually written as a list of the augmentations surrounded by +parentheses. The first item in the list is the identifier that is shared +by all of the augmentations, followed by the pairs of attributes and +values for each augmentation. The objects for the above working memory +elements are: + +```Soar +(S1 ^io I1 ^superstate nil ^type state) +(I1 ^input-link I3 ^output-link I2) +``` + +An individual augmentation can also be written in this form: +`(S1 ^type state)` + +A working memory object usually represents something about the world, +such as a block, a wall, a piece of food, or a cell on the board. The +individual augmentations represent properties (such as color, size, or +weight), or relations with other objects (such as on top of, behind, or +inside). + +Soar can be used for many different tasks, and for each different task, +working memory will contain descriptions of the task objects. The +working memory elements for the blocks would be as follows: + +Working memory usually also contains objects that are only conceptual +things and do not have an identifiable physical existence, such as state +s1, which organizes other objects, relations, and properties. The exact +representation of objects is up to you, the designer of a Soar program. + +Soar does not require any declarations of the possible attributes and +constants. In fact, some Soar programs generate new attributes and +constant values as they execute. The Soar editing and development tool, +VisualSoar, does require declarations for the structure of working +memory elements, and those declarations are used to check for errors in +your rules but they are not used by Soar when it is executing a program. + +## Hello-World Rule: Soar Details + +You now know enough to return to the first example rule. The original +English version included the test + +“If I exist” + +Remember that when every Soar agent is created, it has `(s1 ^type state)` +in working memory, which signifies in someway that the agent does exist. +Thus, you can test for the presence of that structure in working memory +to determine if the agent exists. The obvious condition to write would +be `(s1 ^type state)`. However, s1 is just an arbitrary symbol and might +not be the identifier of that state every time the agent is run.[1] +Thus, we need a test that there is an identifier, but without testing a +specific value. That is exactly what a _variable_ does – it matches any +symbol in working memory with the only constraint being that all +occurrences of the same variable in a rule match the same symbol. A +variable can match an identifier, an attribute, or a value, depending on +its position in a condition - if it is first in a condition it will +match the identifier, second the attribute, and third the value. A +variable is a symbol surrounded by `<` and `>`, such as ``. The +exact symbol used in the variable (such as “s”) is irrelevant but should +be picked to be meaningful to you. + +Putting these pieces together gives: `( ^type state)`. That is almost +correct, but in Soar, every rule must start by matching a structure in a +state, and to remind you of this, the first condition of every rule must +start with the word “state”. + +```Soar +sp {hello-world +(state ^type state) +--> +(write |Hello World|) +(halt)} +``` + +The original text for the rule’s actions was: then write “Hello World” +and halt. + +The rule’s first action prints “Hello World” in the Interaction window. +Vertical bars, “|”, mark constants with special characters. These +constants can contain any characters, which allows you to print spaces, +upper and lower-case letters. The second action is a special command in +Soar that halts the agent. From this simple example, you should be able +to write your own rules that print out different messages for Soar. + +If you wish to try this out yourself, you have two choices. The first is +to use VisualSoar. VisualSoar is the Soar development environment and it +lets you develop and manage Soar programs. It has its own documentation +and tutorial and can be found at the Soar web pages. Also, the Water-Jug +problem that is presented later in this tutorial contains an explanation +of how to use VisualSoar. Although it will initially slow you down to +learn VisualSoar, it will be worth it in the long run. + +If you want to use a text editor, almost any will do, such as Word, +WordPerfect, Wordpad, Notepad, or Emacs. In this editor, create a new +file hello-world.soar in your new folder/directory. Make sure the file +ends in “.soar”. This may be difficult to do in Wordpad or Notepad, +which automatically add “.txt” to the end of a file, but this will be +important for the Eaters and Tanksoar tasks. If you are having trouble +with this in Windows, go to the Settings item off the Start menu. Select +Folder Options and the pick the File Type tab. Select "New Type…" and in +the pop-up window define the soar type. You need to fill in "soar" +(without quotes) under the "Associated extension" and select +"text/plain" under "Content Type." Whenever you save the file, make sure +you save it as a text file with linefeeds. Soar cannot handle formatted +text and text without linefeeds. + +# Building Simple Agents Using Operators + +In this section, you will learn how to use rules to select and apply +_operators_. Operators perform actions, either in the world or +internally in the “mind” of an agent. In the Water Jug problem, they +“pour” water between jugs in the mind of the agent. In Eaters, the +operators will create commands to move the eater around the board. There +could also be operators to remember uneaten food, count food, or +remember where walls are that only make changes to the working memory of +the Eater. Operators in a Tic Tac Toe program would mark X’s or O’s on +the board. Operators for a soccer program would probably include move, +turn, kick the ball, send messages to teammates, interpret messages from +teammates, choose a tactic or strategy, and so on. + +Because operators perform actions, they are the locus of decision +making. They are where knowledge is used to decide what to do (or not to +do). Thus, Soar’s basic operation is a cycle in which operators are +continually proposed, selected, and applied. Rules fire to propose and +apply operators, while the decision procedure selects the current +operator. This may seem restrictive; however, it forces you to separate +out where decisions are being made (proposal and selection) and where +actions are being performed (application). + +For Soar to use operators, they must first be created in working memory +by _proposal_ rules. Proposal rules test features of the state to ensure +that the operator is appropriate and then they create a representation +of the operator in working memory along with an _acceptable preference_ +for that operator. The preference is a way of telling the architecture +that this operator is a candidate for selection. For simple operators, +the representation in working memory is often just a name of the +operator. For example, in section 3.1 we will create an operator to +print “Hello World” and that operator will just have the name +hello-world in working memory. More complex operators can also contain +representations of parameters. For example, when we encode the water jug +problem in Soar, one operator will fill a jug with water and it will +have a parameter as to which jug should be filled. At an extreme, the +representation of an operator can specify exactly which changes should +be made to working memory when it is applied. + +Once the operator is _selected,_ rules that apply the operator will +match and perform whatever actions are appropriate by creating or +removing working memory elements. If the chosen operator is represented +in working memory as just a name, rules that explicitly test that name +will be required to apply that operator. If the operator already +includes a representation of what changes need to be made, then very +general rules can be used to apply the operator that can also be used +with other operators. Thus, working memory acts as an intermediary for +actions by being the place where alternative operators are proposed, one +is selected, and then rules apply the operator by making changes to the +state. + +## Hello World Operator: English Version + +To demonstrate a very simple operator, you will now write an operator +instead of a single rule to print “Hello World”. Operators allow you to +have an action considered in multiple situations (rules that propose the +operator), allow multiple reasons for selecting an action (rules for +selecting operators), and allow multiple ways for doing it (rules that +apply the operator). For printing “Hello World”, an operator is not +necessary because there are no alternatives, but as soon as other +actions are possible and choices must be made, operators become +necessary. + +To use an operator, we need two rules: one to propose the operator and +one to apply it: + +```Soar +Propose*hello-world: +If I exist, propose the hello-world operator. + +Apply*hello-world: +If the hello-world operator is selected, write “Hello World” and halt. +``` + +The first rule proposes the hello-world operator, and the second +performs its actions after the operator has been selected. Notice that +the first rule only proposes the hello-world operator. As described +earlier, an operator is selected by Soar’s _decision procedure,_ which +collects together proposed operators and selects one. + +To see this operator run, you can either create a new agent and then +destroy your previous agent (this is done on the same pull down menu +that was used to create an agent), or you can remove all of the existing +rules in your agent by using the “excise --all” command, which removes +all of the rules from memory and initializes Soar. Next, load in the +hello-world-operator file using source. To see the individual +productions fire, you can modify the “watch” level. There are many +different watch levels: and watch level 3 (and higher) includes traces +of production firings. Go ahead and set the highest level: watch 5. You +can use the “Watch 5” button at the bottom of the interaction window or +use the Debug Level entry in the menu bar at the top. Then click the run +button. + +The interaction window allows you to trace the execution of Soar at many +different levels (decisions, phases, production firings, working memory +changes), but selectively view the trace by expanding and collapsing the +trace (using the “+” and “-” items on the left of the trace) and setting +filters. Make sure you have the “tree view,” which is set under the +layout tab in the top tool bar. A standard practice is to do a full +trace (Watch 5), but only view decisions by collapsing the trace. If an +error arises, or you want to look at the behavior in more detail, you +can expand the trace. The button at the lower right corner of the +interaction window allows you to toggle between “collapse” and “expand” +easily. You can then control the level of the trace by using the +“filters” button that is next to the collapse/expand button. This +allows you to view the trace at a high level and only dive into the +details when necessary. + +After typing run, expand the “run” and “1: O: O1 (hello-world)” lines by +clicking on the “+” signs next to them. The trace shows that +propose*hello-world fires first to propose the operator. The operator +hello-world (O1) is then selected (by the decision procedure). After +hello-world is selected, apply*hello-world fires and performs the +actions of the operator by printing out “Hello World” and halting. + +### ![](Images/01/image7.png) + +## Hello-World Operator: Soar Version + +In this case, there is a one-to-one mapping of the English rules to Soar +rules. The condition of `propose*hello-world` is the same as the +condition for the original hello-world rule. The difference is in the +action, where `propose*hello-world` proposes the hello-world operator. A +rule proposes an operator by creating an _acceptable_ _preference_ for +the operator. An acceptable preference is a statement that an operator +is as a candidate for selection. The rule below proposes the hello-world +operator: + +```Soar +sp {propose*hello-world +(state ^type state) +--> +( ^operator +) +( ^name hello-world)} +``` + +The first action creates an acceptable preference for a new operator +(which is added to working memory) and the second action creates a +working memory element that augments the operator with its name. + +A preference looks just like other working memory elements except that +it has a fourth item: the type of preference, which in this case is “+”. +The identifier of the preference is ``, which means that the +identifier matched to `` in the condition is used in creating the +action. For example, if working memory contained `(s1 ^type state)`, then +when this rule fired the preference would begin with (`s1 ^operator`. The +value of the preference, ``, is a new variable that did not occur in +the condition. When new variables appear in actions, Soar automatically +creates a new identifier and uses it for all occurrences of that +variable in the action. For example, if o1 is the identifier created for +``, then `(s1 ^operator o1 +)` and `(o1 ^name hello-world)` are added to +working memory. For variables in actions, such as ``, a different +identifier is created each time a rule fires. + +Additional rules may create preferences to compare operators. The +decision procedure selects an operator based on all the created +preferences. If a single operator is proposed, that operator is +selected. In this case, `propose*hello-world` fires creating a single +acceptable preference for o1, and then the decision procedure selects o1 +to be the current operator and adds `(s1 ^operator o1)` to working memory. +Note that this working memory augmentation _does not_ have the “+” +following the value. Only the decision procedure can add such an +operator augmentation for a state to working memory (while proposal +rules can add acceptable preferences for operators that differ by ending +with the “+”). + +Once the operator is selected, `apply*hello-world` should fire. + +```Soar +sp {apply*hello-world + (state ^operator ) + ( ^name hello-world) +--> + (write |Hello World|) + (halt)} +``` + +This rule has exactly the same actions as `hello*world`, but with +conditions that test that the operator hello-world has been selected. +The first condition tests that some operator has been selected. We +cannot include a specific identifier for the value (such as o1) because +the exact value of the identifier can be different each time we run the +program, so a variable, in this case , is used. + +The second condition tests that some object in working memory has name +hello-world. The rule will only match if both ``’s match the same +identifier (such as o1). This is true for all variables in Soar; if the +same variable shows up multiple times in the conditions of a rule, the +rule will not match (and fire) unless all occurrences of the variable in +the rule match the same symbol in working memory. In this case, they +both match o1, and this rule fires. + +If the same variable is used in multiple rules, it can match completely +different identifiers or constants – that is, the identity of variables +only matters within a rule. The exact symbols used in a variable are +irrelevant, although as a general convention, `` is usually used to +match state identifiers and `` is usually used to match operator +identifiers. + +## Examining Working Memory + +We can get an even more detailed look at Soar’s processing by examining +the data structures in working memory. You can print out working memory +in the Interaction Window using the print command. To print out all of +the attributes and values that have s1 as the identifier, first click in +command box below the interaction window, and type +print s1 followed by the “Enter” key. The +debugger also has additional windows to the right of the Interaction +Window that display common structures, such as the current state, the +current operator, etc. The topmost window on the right should already +show the state. This window has tabs for examining different structures +in working memory. You can modify the command in these windows by typing +in a new command (such as replacing “print s1” with “print s1 –d 2”), or +you can add new tabs or create a new window that executes different +commands. You do this by right clicking on a blank part of the window, +and a menu will appear that let’s you create a new window or a new tab. + +![](Images/01/image8.png) + +You now see all of the attributes and values that have S1 as their +identifier. The attributes io, superstate, and type are created +automatically for the state before the program starts to run. The +operator attribute is created when the hello-world operator (o1) is +selected. Only the **selected** operator has a working memory element +_without_ the following “+”. In addition to the selected operator, the +acceptable preference for the operator is there, marked with the "+". If +there were additional operators proposed with acceptable preferences, +there would be additional working memory elements with the operator +attribute with the ids of the operators followed by the “+”, but for a +given state there is only one working memory element with the operator +attribute without the “+”, which signifies the _selected_ operator. The +attributes are ordered alphabetically when they are printed, but that is +just to make it easier to find a specific attribute. + +We can examine more of the structure of o1 using the print command. This +time, select the operator tab or type p +o1. The letter p means “print.” Almost all commands in Soar also +have one or two-character shortcuts. + +![](Images/01/image9.png) + +Operator o1 has an augmentation with attribute name and value +hello-world. We can also examine the augmentations of some of the other +values. I1 is the value of the ^io attribute, which stands for +input-output. To see more about input and output, print I1. However, +instead of using the print or p command, you can use the mouse to print +information about I1. (If you don’t have a mouse, just use print.) To do +this, type print s1 again as before and move the cursor over I1 in the +upper right window, then press the right mouse button. A menu will +appear that has a list of commands: print, preferences, WMEs, +productions, and run. Move the mouse over the print command and click +the left button once. + +![](Images/01/image10.png) + +The information for I1 now appears in the Interaction window to the left +where the trace is displayed. + +![](Images/01/image11.png) + +There are two attributes of io: input-link and output-link. The +input-link is where an agent’s sensory information is available in +working memory. The output-link is where action commands must be created +for the agent to move in its world. Using the mouse, explore the +structure of the input-link (I2). You can also try out some of the other +commands available through the mouse, although we will work our way +through them later in the tutorial. + +You now know the basic structure of rules and operators in Soar. You +should be able to create your own agents that print out simple messages +with operators. + +## An Introduction to VisualSoar + +To create your own Soar programs, you can use almost any editor. We +recommend VisualSoar because it has internal support for creating Soar +programs. VisualSoar can be found in the same place that the Soar +debugger was found. On launch, a fairly blank window entitled VisualSoar +will pop-up. At the top, click File | New Project… and you will see a +new pop-up window entitled New Agent. In the new window under Agent Name +type “water-jug” (without the quotes) and under Agent Path, click Browse +(if you receive a “no disk” error, ignore it by simply clicking +continue) and locate the folder to which you want to save the project +to. When this is done, click New. + +You will see a new window on the left that should contain 5 things in +the form of a tree. This is called the operator window. At the root is +the name of your project, and the four things below it are the default +files that are automatically created. The specifics of these four files +will be explained as the tutorial proceeds. It is files like these that +will contain the rules you write for an agent. The rules will be grouped +into different files and file types depending on their function. It is +from this window that you will control the way the rules are grouped +together. The actual grouping of rules in VisualSoar is actually +completely arbitrary to Soar but will make maintaining and debugging +your code much easier for you. Due to this arbitrariness, do not feel +that you can create an error by grouping your rules wrong. + +One of the toughest parts of writing Soar code is visualizing the +tree-like structure of working memory. VisualSoar has a structure called +the Datamap for describing working memory structure. In some ways, it is +like making type definitions in other languages. To examine the Datamap, +right-click on the root of the tree entitled ‘water-jug’ and click on +Open Datamap in the pop-up window. Doing this displays the hierarchical +structure of the working memory of your agent in the Datamap water-jug +window. VisualSoar not only lets you visually construct what memory +should look like, it also lets you run tests to make sure your code +follows the proper structure, automatically finding spelling mistakes, +misplaced structures, and so on. You can close the Datamap water-jug +window by clicking on the \[x\] in its top-right corner. + +Throughout the rest of Part 1 of this manual you will write code to +solve the water jug problem. Write this code into your Soar editor as +you go. Once you get the hang of it, try and write the next step in the +code before you look at it (make sure to double-check it with our code +of course). + +![](Images/01/image12.png) + +## Creating Agents to Solve the Water Jug Problem + +This part of the tutorial will teach you how to build Soar programs that +solve problems through internal problem solving. You will build a Soar +program for a classic AI problem: The Water Jug. Other classic AI +problems, including Blocks World, the Eight Puzzle, Missionaries and +Cannibals, and Towers of Hanoi are included in the set of demonstration +programs that comes with the Soar release. The Soar programs you develop +will solve these problems through search and the manipulation of +internal data structures. You will start by building the operators, +state descriptions, and goal tests that are required to define the +problem. You will also be introduced to more of the theory of problem +solving based on search in problem spaces. In Part V, you will learn how +to modify and extend to your programs so that they use planning and +learning to solve problems. If you run into problems debugging your +programs, you might want to skip ahead to Part II, Section 5.3, which +contains details on debugging commands for Soar. + +### Water Jug + +Water Jug Problem Statement: + +> You are given two empty jugs. One holds five gallons of water and the +> other holds three gallons. There is a well that has unlimited water that +> you can use to completely fill the jugs. You can also empty a jug or +> pour water from one jug to another. There are no marks for intermediate +> levels on the jugs. The goal is to fill the three-gallon jug with one +> gallon of water. + +The first step in formulating a problem is determining the space of +possible states that the problem solver can be in. The space of states, +or _problem space,_ is determined by the objects that are available to +be manipulated (the two jugs) and their possible values (0-5 gallons +full). A problem is then defined as an initial state that the problem +solver starts at, (in this case two empty jugs) and a set of desired +states (any state where the three-gallon jug has one gallon of water). +Problem solving consists of starting at the initial state, then +searching for a desired state by applying _operators_, which transform +one state into another. For the water jug problem, the operators are +_fill_ a jug, _empty_ a jug, and _pour_ from one jug to the other. + +In addition to the knowledge required to formulate the problem, +knowledge can be used to select which operator to apply at each state. +In the water jug, you might have knowledge that specifies never emptying +a jug immediately after it has been filled. In Soar, this knowledge is +often called _search control_ _knowledge_, because _operator selection +is the locus of all decision making in Soar._ It is the _one_ place +where multiple alternatives can be proposed, compared and evaluated +before a selection is made. An important part of Soar’s design is that +it makes it possible to separate all of the problem formulation +knowledge from the knowledge that is used to control the search. + +At an abstract level, a trace of the problem solving starts with the +creation of the initial state and is followed by the selection and +application of operators. To represent the states in the water jug +problem, we will have two values: the amount of water in the five gallon +jug, and the amount of water in the three gallon jug. For example: +5:0,3:0 will represent that the 5 gallon container has 0 gallons of +water, and the 3 gallon container has 0 gallons of water. Below is a +trace of the optimal solution for the problem showing the states and the +operators that are applied to each state. + +``` +(5:0,3:0) Fill 3 gallon jug +(5:0,3:3) Pour 3 gallon jug into 5 gallon jug +(5:3,3:0) Fill 3 gallon jug +(5:3,3:3) Pour 3 gallon jug into 5 gallon jug +(5:5,3:1) +``` + +The rest of this section will take you through all of the steps of +representing the problem in Soar, including determining the +representation of the states in working memory, creating the initial +state, representing the operators in rules (and working memory), and so +on. + +### State Representation + +For tasks that involve interaction with the outside world, much of the +state structure is determined by what comes in via perception. For the +water jug problem, we could hook up Soar to a water jug simulator (or +real world robot) and have it issue commands to pour water into and out +of jugs, but to simplify learning Soar, we will have Soar solve the +problem through the manipulation of internal data structures. . +Moreover, this will prepare you for planning, where your program will +need to create an internal representation of the problem even if there +is an external world. + +What are the parts of the problem that must be represented on the state? +There are really two types of information: dynamic information that +changes during problem solving, such as how much water is in each jug, +and static information that doesn’t change, such as the size of each +jug. + +So one state representation might include: + +- The amount of water each jug currently holds. +- The amount of water each jug can hold. + +You can combine these together by having the volume of the jug as an +attribute with the value being the current contents. Under this scheme, +the initial state would look something like: + +```Soar +(state ^five-gallon-jug-contents 0 +^three-gallon-jug-contents 0) +``` + +This is a very compact representation; however you will have to write +very specific rules for proposing and applying operators. You will need +one set of rules to propose the operators for the five-gallon jug and +one for the three-gallon jug. + +A more general approach would be to represent each jug as an object that +had two attributes: + +- The amount of water each jug currently holds (^contents). +- The amount of water each jug can hold (^volume). + +In this scheme, the initial state would look something like this. + +```Soar +(state ^jug + ^jug ) +( ^volume 5 + ^contents 0) +( ^volume 3 + ^contents 0) +``` + +Note that jug is an attribute that can have multiple values at the same +time. We call this a _multi-valued_ _attribute_. Multi-valued attributes +allow you to represent sets of objects, such as the set of two jugs. + +Using this representation, you can write one set of rules for +manipulating both jugs. Although this representation is sufficient for +defining and solving the problem, as you will see later, the rules to +propose operators can be simplified if you add an attribute for the +amount of water that can be added to a jug to fill it (^empty). As you +will see later, the value of this augmentation can be computed +dynamically using _state elaboration_ rules that test the volume and +contents augmentations and subtract the volume from the contents. Thus, +states will include the following structures: + +- An object for each jug (^jug). +- The amount of water each jug can hold (^volume). +- The amount of water each jug currently holds (^contents). +- The amount of empty space available in each jug (^empty). + +Although this representation is sufficient, one more working memory +structure is extremely useful: a description of the task that is being +attempted, in this case water-jug. By having some description of the +task, the rules we create can be specific to that task and easily +combined with rules from other tasks without interference. Thus, a +common Soar _convention_ is to label the state with information about +the task being attempted. The simplest approach is to have a ^name +attribute, where the value is the name of the task. For some tasks, +other information is useful, but this will be sufficient for the water +jug task. + +- The task name (^name water-jug) + +### Initial State Creation: The initialize-water-jug Operator + +In order for Soar to start on a problem, the structures of initial state +must be in working memory. For tasks involving interaction with an +external environment, much of the state will be created by the +perceptual information that comes in from sensors. That information +would be created on the input-link structure of the state. But for this +task, we are assuming there is no external environment, so the initial +state must be created by rules that you add to the system. + +Thus, the first step is to define an operator that will create the +initial state. By using an operator instead of a single rule, the act of +initialization is a choice that can be controlled – that is, the +operator to initialize the problem will be proposed and a decision will +be made whether to do it. If other tasks are possible, the agent can +decide which task will be attempted by using control knowledge to pick +among the different initialization operators. + +The first step in creating an operator is to write down the proposal +rule in English. + +Type the English versions of the proposal and application rules for the +initialize-water-jug operator. These versions will be documentation, so +we have to tell Soar to ignore them. You can do this by starting each +line with the pound-sign (“\#”). Soar ignores lines that start with a +pound sign. + +In Soar, the name of the rule can be almost any string of characters, +but it is a good idea for it to be a meaningful summary of the purpose +of the rule. A useful convention is to split the name into parts +separated by asterisks (“\*”). The first part is the task, the second +part is the function (propose, apply, elaborate), and the third is the +name of the operator. There may be additional parts for more details of +what the rule does. + +As with every operator, we must define two types of rules: one to +propose the operator and one to apply the operator. This operator should +be proposed only at the beginning, before any task is selected. Thus, +the proposal should be: + +``` +water-jug*propose*initialize-water-jug +``` + +If no task is selected, + +then propose the initialize-water-jug operator. + +Writing this rule in Soar is a challenge given what you know so far. How +can you test that no task has been selected or that no jugs are defined? +In Soar it is possible to test for the _absence of working memory +elements_ by preceding the attribute with “-“. Every rule in Soar must +have at least one positive condition, so you still need to test that the +state exists and in this case we use (^superstate nil). + +```Soar +sp {water-jug*propose*initialize-water-jug + (state ^superstate nil) + -( ^name) +--> + ( ^operator +) + ( ^name initialize-water-jug)} +``` + +**VisualSoar** – The purpose of this production is to propose the +operator that will initialize the water-jug. If you look at the operator +window in VisualSoar you will see that the file at the bottom is titled +“initialize-water-jug.” This is created automatically when you created +the water-jug project because a standard practice is to use an +initialization operator for a new task. Either double-click on that file +or right-click on the file and click Open Rules. This will open up a +rule editor in which you will see two rules already partially written. +The initialization written above is a very standard one, and because of +this VisualSoar automatically creates a template of it for you (it is +the first rule at the top). It may look a little different because +VisualSoar uses a few shortcuts that you will learn later, feel free to +leave it how it is or change it to the way it is above, it will not +matter. Leave this rule window open. + +Just a reminder that the order of the conditions don’t matter except +that the first condition must be a _positive test of the state_. Thus, +there is no difference between the above rule and one where the test for +the absence of the jug comes before the absence of the name. Soar +analyzes the rule and automatically reorders the conditions in order to +improve the efficiency of matching the rules. + +The second condition will match only if there is not any working memory +element on the state with attribute name. The condition does no test for +a value, because it doesn’t matter what the value is. You could include +variables in the value position (such as ) if you wanted, and +that would not change the meaning of the rule. + +All the conditions test attributes of the same object (the state), and +you can simplify the rule by combining these conditions (just as +VisualSoar did) into a single structure where the identifier `()` is +followed by the attributes: + +```Soar +sp {water-jug*propose*initialize-water-jug +(state ^type state + +-^name) +--> +( ^operator +) +( ^name initialize-water-jug)} +``` + +Soar automatically translates this structure into the one above. + +To apply the initialize-water-jug operator, we need another rule. The +initialization application rule adds the name to the state and creates +the jugs with contents of 0. The rule needs to add working memory +elements for the initial values of the jugs and their attributes. + +``` +water-jug*apply*initialize-water-jug +``` + +If the initialize water-jug operator is selected, + +then create an empty 5 gallon jug and an empty 3 gallon jug. + +```Soar +sp {water-jug*apply*initialize-water-jug +(state ^operator ) +( ^name initialize-water-jug) +--> +( ^name water-jug +^jug +^jug ) +( ^volume 5 +^contents 0) +( ^volume 3 +^contents 0)} +``` + +This rule does not include the creation of the ^empty attribute – it +will be created by another rule that dynamically computes it based on +the volume and contents (see Section 4.5). + +**VisualSoar** – In the initialize-water-jug rule window you will notice +that VisualSoar has attempted to write this rule for you also. The +application of initializing the water-jug is not as standard as the +proposal and VisualSoar is not able to initialize all of the attributes +that this project will need. Modify the second rule in the rule window +to match the rule above. + +Now, at the top of VisualSoar, click on Datamap | Check All Productions +Against the Datamap. In the feedback window at the bottom you will see a +list of blue warnings. What you just did was make VisualSoar check all +of your rules to see if they matched the Datamap for this project. +Recall that the Datamap is a representation of the structure of working +memory. Since you just added new augmentations to the state in your +rule, but not to the Datamap, warnings are created. To fix this, +right-click in the operator window on “water-jug” and click on Open +Datamap so that now you have the Datamap and initialize-water-jug +windows open. You will notice that VisualSoar has already included the +structure for the Initialize-water-jug operator – this is automatically +created whenever a new operator is added to the operator set. The +Datamap is a hierarchical structure that looks like folders and files as +in the operator window, but it is really the structure of working +memory. The folders are used to represent working memory elements (such +as operator) whose values are identifiers, and thus can have +substructure. The file-like structures are attributes that have +constants as values – either sets of symbols (enumeration), integers, +floating point numbers (floats), or strings. You must now add entries +into the Datamap for the water-jug state structure. + +![](Images/01/image13.png) + +What you need to add to the Datamap are the jug structures that are +created by initialize-water-jug and that will be used by the remaining +operator. In order to add a new attribute to the Datamap, you start by +right clicking on the “water-jug” folder in the Datamap (not in the +operator window). The water-jug folder in the Datamap represents the +top-state structure in Soar. After right-clicking, a menu will appear +that let’s you select the type of value that will be associated with the +attribute you are creating. Since the jug attribute will have an +identifier as its value, select “Add Identifier …” + +![](Images/01/image14.png) + +You will then be prompted for the attribute name. Type in “jug”. + +![](Images/01/image15.png) + +This will add the jug structure to the Datamap. You can now right click +on the jug structure, and select “Add Integer …” to add the contents +attribute to the jug structure. Type in the name, and then repeat this +for the empty and volume attributes. If the attribute had values that +were different possible symbols, you would use the “Add Enumeration …” +selection. Your screen should now look like this: + +![](Images/01/image16.png) + +Now have VisualSoar check all productions against the Datamap (using the +command under the Datamap tab in the toolbar), you should have no +errors. Make sure to save and feel free to close the Datamap and +initialize-water-jug windows. + +In the above rule, the variable is used to connect the first +condition to the second. To simplify the writing and reading of rules, +Soar allows you to combine conditions that are linked by variables. To +do this, you can just string together attributes, replacing the +intermediate variables with a period, “.”, to separate the attributes. +In Soar, we call this “dot” or “path” notation. Below is the same rule +as before using dot notation. + +```Soar +sp {water-jug*apply*initialize-water-jug + (state ^operator.name initialize-water-jug) +--> + ( ^name water-jug + ^jug + ^jug ) + ( ^volume 5 + ^contents 0) + ( ^volume 3 + ^contents 0)} +``` + +## Persistence of Working Memory Elements + +When the water-jug*apply*initialize-water-jug rule fires, all of the +structures in its action are added to working memory. At this point, it +would be desirable for the initialize-water-jug operator to be removed +from working memory so that other operators can be selected. This is +exactly what Soar does because the conditions of the proposal rule no +longer match (the conditions testing for the absence of a task and the +absence of the jug augmentation fail to match because those structures +have been added to working memory). Once that rule _retracts_, the rule +that applied the operator also no longer matches because the operator is +no longer in working memory. However, we do not want the application +rule to remove the structures it created when it no longer matches. If +it did, the system would never be able to make any real progress – it +would get in an infinite loop of proposing and applying an operator, +retracting its results, and then proposing and applying the operator +again. + +In order to support the different needs of different problem solving +functions (in this case operator proposal and operator application), +Soar makes a distinction between the _persistence_ of working memory +elements created by operator application rules and the persistence of +working memory elements created by other types of rules. Operator +application needs to create persistent results because operators are the +deliberate commitments that the system has made – it has decided to +apply the operator and make potentially non-monotonic changes to the +state – to move itself to a new state in the problem space. All other +rules in Soar compute the entailments/elaborations of the current state +without actually changing it and should be retracted when they no longer +match the current state. This is a very important feature of Soar – it +makes a distinction between knowledge that modifies the current state +(knowledge in operator applications), and knowledge that just computes +the entailments of the current situation (including which operators +should be considered for the current state). + +Soar automatically classifies rules as to whether they are part of an +operator application or not. A rule is an operator application rule if +it tests the selected operator and modifies the state. The working +memory elements created by such a rule persist and are said to have +_operator-support,_ or _o-support_ because they are created as part of +an operator. These working memory elements can be removed by other +operator applications rules (this type of removal has yet to be +introduced in the tutorial), or if they become disconnected from the +state (because of the removal of other working memory elements). + +For non-operator application rules, including rules that propose an +operator, rules that compare operators, rules that elaborate operators, +or rules that elaborate the state, the working memory elements created +by the rules are removed from working memory if the rule no longer +matches. These working memory elements are said to have +_instantiation-support_ or _i-support_ _–_ meaning they will persist +only as long as the rule instantiation that created them still matches. + +We will examine the persistence of working memory elements in more +detail when we create the rules for the Water Jug, but this is a +fundamental and sometimes difficult part of Soar for people to learn. It +differentiates Soar from most other rule-based systems that don’t have +rules that automatically retract their results when they no longer match +and it gives Soar its own Truth-Maintenance System (TMS). + +### + +### + +## Water Jug State Elaboration + +The initialization operator creates jugs that include the volume and +content. As mentioned earlier, it will be useful for each jug to include +the amount of additional water that can be added to it before it is +full. Although this could be created by the initialization operator, it +would have to be maintained by every operator application and that would +make the operator application rules more complex. An alternative is to +compute the available volume for each jug in the state with a separate +rule. This rule will test the state and create a new structure on the +state. This type of rule is called a _state elaboration rule._ State +elaboration rules are ubiquitous in large Soar systems because they can +create useful abstractions of combinations of other working memory +elements and represent them directly on the state as a new augmentation. +These new augmentations can then be tested in other rules in place of +complex combinations of conditions, simplifying rules and making the +structures in working memory more meaningful. A critical aspect of state +elaboration rules is that they create i-supported working memory +elements so that when the parts of the state they test change, they +recompute their actions automatically. + +For the Water Jug problem, the state elaboration will compute the amount +of empty space in a jug: + +water-jug*elaborate*empty + +If the state is named water-jug and a jug can hold volume v and +currently has contents c, then add that it has v – c available (empty) +space. + +The first condition that it tests is that the state is named water-jug, +so that it will only apply for water-jug problems. This test will be +included in all of the water jug rules. It then tests that there is a +jug and adds an appropriate empty attribute. Translating the conditions +of this rule to Soar is straightforward. We have to include conditions +for the jug, its volume and its contents. + +The action requires adding a new attribute to the jug (thus using the +same variable as matched in the condition, ``) and then computing the +value by doing some math. In Soar, all math is done using prefix +notation where the math operations `(+, -, *, /)` come before the +arguments, with parentheses surrounding the computation. For example, if +the value was going to be the sum of `` and ``, you would put +`(+ )` where the value should be (right after the `^empty` +attribute). You can nest operations using additional parentheses, such +as `(+ 2 (* ))` to add 2 to the product of `` and ``, +but for this case, nesting is not necessary. See if you can write the +rule yourself before looking below: + +```Soar +sp {water-jug*elaborate*empty + (state ^name water-jug + ^jug ) + ( ^volume + ^contents ) +--> + ( ^empty (- ))} +``` + +When the contents of a jug changes through an operator application rule, +this rule will retract the old value of ^empty and fire to compute a new +value. + +**VisualSoar** – This rule is an elaboration. VisualSoar has a folder +named elaborations in the operator window to hold files for state +elaborations. Right-click on this folder and click “Add a File…”. You +will see a text box pop-up. In the Name category type “empty” and click +OK. Expand the elaborations folder and you should see the file you just +created and two default files named “\_all” and “top-state.” These files +are provided for all Soar programs and have a few simple rules that are +useful for tasks with substates. You should now open the “empty” file by +double clicking on it. You can add the rule from scratch and just type +it in, or you can use a template. To use a template, click on Insert +Template in the toolbar and select elaborate-state. That will give you +the basic structure of an elaboration rule. Just as before, check your +file against the Datamap. + +## Water Jug State Initialization and Elaboration + +At this point you should have written your initialization rule and the +state elaboration rule into your Soar editor. Make sure to save this +file then start up the Soar Debugger and load these rules in using the +source command. + +Now set the watch level to 5 and expand the trace so you can see rules +fire and the creation on working memory elements. + +As you step through the execution, + +1. The water-jug*propose*initialize-water-jug rule fires to propose + the initialization operator. Expand the plus sign next to 1:O: O1 + (initialize-water-jug) +2. The operator and the acceptable preference for it are added to + working memory: (s1 ^operator o1 +), (o1 ^name + initialize-water-jug). The “+” signifies that the operator is + proposed and not selected. +3. The initialization operator is selected by the decision procedure. + +A working memory element is added to working memory to indicate that the +operator is selected: (s1 ^operator o1). Note that the operator +preference is still in working memory. + +4. The application operator fires and creates the jugs on the state and + the name. +5. The operator proposal retracts because the condition that tests for + the absence of the name fails and the water-jug*elaborate*empty + rule fires twice, once for each jug, and computes the empty + augmentation for each of the jugs. +6. A substate is created (ignore for now) because there are no other + rules to fire. + +Note that in number 4, `water-jug*elaborate*empty` fires twice because +there are two jugs and this rule matches successfully with each jug. +Each of these matches is called an _instantiation._ An instantiation +consists of a set of working memory elements that successfully match a +rule. In this case, there will be two sets of three working memory +elements that are completely separate. Sometimes a rule will have +multiple instantiations that share some of the same working memory +elements, while in other cases such as this one, they are all different. + +Given that there are two instantiations, which one should fire first? +There really is no way to say that one is more important than another, +and in Soar, we attempt to avoid decisions when we really don’t have to +make them, so in Soar, all rules that newly match fire at the same time, +_in parallel._ Of course the Soar program is running on a serial +computer (usually), so it isn’t really in parallel, but for all intents +and purposes, the rules fire in parallel. This is very different than +most programming languages and can be confusing (and liberating) because +you will find that rules fire as soon as they match. + +Below is a more complete look at Soar’s basic cycle. For this problem +there is no input and output, so those boxes are skipped. Starting at +the left, rules fire (or retract) that elaborate the state or propose +operators. There might be multiple rules firing (or retracting) in +parallel, and the results of one rule firing (or retraction) might lead +to additional rules firing (or retracting), and Soar continues to fire +these waves of rules until there are no new firings (or retractions). +Once there are no more rules to fire (or retract), quiescence is reached +and the decision procedure is entered to select the operator. This +approach guarantees that when a decision is made, all of the available +knowledge from rules is available. + +After the operator is selected, rules that apply the operator can fire. +Operator application will usually lead to firing and retracting of state +elaboration and operator proposal rules and that will also happen during +the operator application phase. When that phase reaches quiescence, +output and then input are processed followed by the first phase +described above. In tasks where there is an external environment, many +operators require action in the world, and so changes to the state will +appear during input. In the water jug task, all state changes happen in +operator application, so that the elaborate state and propose operator +phase do not lead to additional rule firings after the first time +through the cycle. + +## Water Jug Operator Proposals + +In the Water Jug problem, there are three operators: fill, empty, and +pour. In this section we will create the rules that propose these +operators. One thing Soar lets us do is separately compute when an +operator can apply (proposal rules that create acceptable preferences +for operators) from when it should apply (search control rules that +create other types of preferences). + +Listed below with each operator are the conditions under which an +operator is legal. These conditions will be the basis for the operator +proposals. + +- _Fill_ a jug with water from the well, if that the jug is not full. +- _Empty_ the water from a jug to the well, if there is water in the + jug. +- _Pour_ water from one jug to another jug, if there is water in the + source jug and the destination jug is not full. + +Although one could attempt to fill a jug that is already full; in Soar, +operators _must_ change the state in some way so that another operator +can be selected. Thus, in our formulation, filling a full jug must be +disallowed. Now write English descriptions of the proposals. + +``` +water-jug*propose*fill +``` + +If the task is water-jug and there is a jug that is not full, + +then propose filling that jug. + +``` +water-jug*propose*empty +``` + +If the task is water-jug and there is a jug that is not empty, + +then propose emptying that jug. + +``` +water-jug*propose*pour +``` + +If the task is water-jug and there is a jug that is not full and the +other jug is not empty, + +then propose pouring water from the second jug into the first jug. + +Each of these rules can potentially match either one of the jugs. + +In translating these rules into Soar, we need to write the tests in the +conditions, but we also need to write actions that will create the +operator. The hello-world and initialization operators were just a name; +however, with these new operators, you want to have some _parameters_ +that specify which jugs are being filled, emptied, or poured. If you +really wanted to, you could encode everything in the name of the +operators, but then we would need six different operators, such as +“fill-jug-three” or “pour-jug-five-into-jug-three”, and this would +require separate rules to apply each of those operators. Instead, by +having parameters _with the operators, you can have_ fewer rules that +apply the appropriate changes to the state. For this task, a set of +operator parameters and possible labels are listed below: + +- The name of the operator: `^name fill/empty/pour`. +- The jug that is being filled by the fill operator: `^fill-jug `. +- The jug that is being emptied by the empty operator: `^empty-jug `. +- The jug that is being poured out of by pour: `^empty-jug `. +- The jug being poured into by pour: `^fill-jug `. + +For example, with this representation, the operator representation for +pouring from jug to would be: +( ^name pour +^empty-jug +^fill-jug ) + +There are lots of other possibilities and it is not necessary to share +the name of the parameters between the pour operators and the others. + +Now try to write the first proposal as a Soar rule for proposing fill. +The only test besides the name of the state is that the jug is not full. +Using what you know already, one way to write this rule is: + +```Soar +sp {water-jug*propose*fill +(state ^name water-jug +^jug ) +( -^empty 0) +--> +( ^operator +) +( ^name fill +^fill-jug )} +``` + +This rule tests that jug does not have an empty augmentation of 0. +This is a double negative, and although correct in the context of what +has been written, it is difficult to understand. An alternative is to +test that the ^empty augmentation has a value greater than 0. Soar +supports simple tests in conditions including greater than (>), greater +than or equal (>=), less than (<), less than or equal (<=), and not +equal (<>). The tests are associated with the value (or variable) that +_follows_ them. So an alternative way to write this rule is: + +```Soar +sp {water-jug*propose*fill +(state ^name water-jug +^jug ) +( ^empty > 0) +--> +( ^operator +) +( ^name fill +^fill-jug )} +``` + +Another alternative condition would be to replace the test of empty +being greater than zero with a test that the contents don’t equal the +volume: ( ^volume ^contents <> ). + +One alternative that is not possible is to do some simple math in the +conditions to test whether the volume minus the contents equal 0. Soar +does not allow any math in conditions – you should use elaborations +instead so that the math is always done in actions. + +**VisualSoar** – If you look in the operator window in VisualSoar, you +will not see anything relating to a “fill” operator . You need to create +a new operator file that will hold all of the rules related to fill. To +do this, right-click on “water-jug” in the operator window and click on +“Add a Suboperator…”, name it “fill” and click OK. Open the newly +created fill operator and add the rule above (using the propose operator +templates). + +When finished adding the rule, open the Datamap. You will notice that an +operator structure for fill has automatically been created, but you need +to add empty-jug as an attribute. The value for empty-jug is an +identifier, but it is really the same structure as a jug on the state. +So instead of creating a new empty-jug structure from scratch, we want +to create a “link” from empty-jug to the state jug structure. Note, we +don’t want a copy of the jug structure, because if the jug substructure +changes, we want those changes reflected in the empty-jug structure. To +create the link is the “trickiest” operation in VisualSoar. First, +left-click and hold (don’t let up on the left-mouse button\!) on the +“jug” structure on the state. Then with drag that structure onto the +fill operator structure in the Datamap. If you let up on the button now, +it would move the jug structure, but we want to create a link. To do +that, hold down the “ctrl” and “shift” keys (usually with your left +hand). This should make a new icon appear that has a little arrow (see +diagram below). Then let up on the mouse button (while keeping “ctrl” +and “shift” down. This will create an attribute of the operator with +name “jug”. (Note: sometimes the display of the Datamap in VisualSoar is +not correctly updated. You might need to close the Datamap and reopen it +for it to refresh correctly). + +![](Images/01/image17.png) + +At this point, you should rename the attribute. Do this by +right-clicking on the attribute, and then select “Rename Attribute …” +from the menu. Type in “fill-jug” and then click OK. + +![](Images/01/image18.png) + +Now try to write the proposal for emptying a jug. This requires only +minimal changes to the previous rule, changing the test that a jug is +not full to a test that the jug is not empty. + +```Soar +sp {water-jug*propose*empty +(state ^name water-jug +^jug ) +( ^contents > 0) +--> +( ^operator +) +( ^name empty +^empty-jug )} +``` + +**VisualSoar** – This is very similar to before. Add a suboperator (just +like “fill”) called “empty” to water-jug, type in the rule, and create +the Datamap entries for it. + +Writing the third proposal for pour requires a test that one jug is not +empty (so it can have water poured from it) and the other jug is not +full (so water can pour into it). You might think that this is a simple +combination of the tests in the previous two rules so that the rule +should be: + +```Soar +sp {water-jug*propose*pour\*wrong +(state ^name water-jug +^jug +^jug ) +( ^contents > 0) +( ^empty > 0) +--> +( ^operator +) +( ^name pour +^empty-jug +^fill-jug )} +``` + +This rule is not correct because the tests for jug `` and jug `` +can match the same jug. That is because there is nothing in Soar that +prevents different conditions from matching the same working memory +element. For example, if working memory holds `(s1 ^jug j1)` `(j1 ^contents +3 ^empty 2 ^volume 5)`, this rule would create a proposal for a pour +operator that would pour from jug j1 to itself because it would +successfully match the conditions for both `` and ``. To correct +this rule, the conditions must be modified so the `` and `` can +not match the same identifier, that is, that `` is not equal to +``. That test in Soar is `<> `. But where should that test be +added in the rule? In Soar, multiple tests of a value (and multiple +tests for an identifier or attribute) can be specified by surrounding +all of the tests with curly brackets: { … }. In this case, we can +combine the test for `` with `<> ` to be `{ <> }`. +This demonstrates both the use of curly brackets and the use of not +equals. Note that the test could also be `{ <> }` and it +would mean exactly the same thing – what matches in this position cannot +equal `` and must equal ``. + +```Soar +sp {water-jug*propose*pour +(state ^name water-jug +^jug +^jug { <> }) +( ^contents > 0) +( ^empty > 0) +--> +( ^operator +) +( ^name pour +^empty-jug +^fill-jug )} +``` + +**VisualSoar** – Once again this is very similar to before. Add a +suboperator (just like “fill”) called “pour” to water-jug, type in the +rule, and add the Datamap entries for it. + +Now that you have all of the proposals written, you can load them in +with your other rules. What happens when you run them? You will see that +only `water-jug*propose*fill` matches in the initial state. It fires +twice since it matches both jugs, right after ^empty is added to each +jug. What happens next? When Soar does not have enough knowledge to +select between the two operators, it has a _tie impasse_ and it +automatically creates a substate where it can do reflective problem +solving to decide what to do next. We will cover substates in a later +section. For now, we just want Soar to select randomly between these +operators. You can tell Soar that it doesn’t matter which operator is +selected by creating an _indifferent_ preference, using “=” for the +operator. This can be created in the same rule that proposes the +operator. + +```Soar +sp {water-jug*propose*fill +(state ^name water-jug +^jug ) +( ^empty > 0) +--> +( ^operator + +^operator =) +( ^name fill +^fill-jug )} +``` + +The two actions for `^operator` can be shortened by using just: +`( ^operator + =)` + +which means create an _acceptable_ and _indifferent_ preference (you +always need the acceptable preference, even if there are other +preferences created). You should modify all of the operator proposals to +include the = preference. + +## Operator Application + +The next step is to write the operator application rules that will fire +once an operator is selected. In the Water Jug problem, your rules apply +operators by adding and removing working memory elements to reflect the +pouring of the water. + +What keeps the operator application productions from applying over and +over again? When the operator rules apply, they change the part of the +state either directly (through rules that change working memory) or +indirectly (by doing some action in the world that changes what the Soar +agent perceives – more on that in later sections). These changes will +cause the acceptable preference for the selected operator to retract +because at least one of the working memory elements that matched the +proposal rule changed. The changes to working memory will cause new +matches of proposal rules, which will be followed by the selection of a +new operator, its application, and so on. + +Given the state representation used in this problem, there are two +different ways that the operators can change the state representation. +The most straightforward is to add and delete the ^contents attributes +of the jug objects. However, the operators could also remove a jug and +create a completely new jug object with the correct contents and volume +attributes. Although this is less efficient, it has some advantages for +planning and we will return to this approach in Part IV. For now you +should write operator application rules that modify the ^contents +attribute of the jug objects (it is not necessary to modify the ^empty +attribute because it will be automatically recomputed whenever the +^contents change). + +In the Water Jug, there are three separate operators and each one +requires its own operator application rules. We will examine them one at +a time. The first is fill. Try to write an English version of its +operator application rule. + +``` +water-jug*apply*fill +``` + +If the task is water-jug and the fill operator is selected for a given +jug, then set that jug’s contents to be its volume. + +This rule requires replacing the current value of the contents with a +new value (the volume). To replace a value in Soar, you must remove the +original working memory element and create a new one. There is no way to +just replace the value field in an existing working memory element. This +is an important attribute of Soar – all changes require the addition or +deletion of working memory elements. You cannot modify a working memory +element once it is created. You already know how to create a working +memory element. To delete one, you specify the working memory element in +the action but with a minus “-“ at the end. + +```Soar +sp {water-jug*apply*fill +(state ^name water-jug +^operator +^jug ) +( ^name fill +^fill-jug ) +( ^volume +^contents ) +--> +( ^contents ) +( ^contents -)} +``` + +**VisualSoar** – In VisualSoar, we group all of the rules for an +operator in a single file. In this case, you should reopen the fill +operator file, and add this rule (using a template) directly under +water-jug*propose*fill. + +The rule to apply the empty operator is essentially the inverse of fill. + +water-jug*apply*empty + +If the task is water-jug and the empty operator is selected for a given +jug, then set that jug’s contents to be 0 and its empty to be its +volume. + +```Soar +sp {water-jug*apply*empty +(state ^name water-jug +^operator +^jug ) +( ^name empty +^empty-jug ) +( ^volume +^contents ) +--> +( ^contents 0 +^contents -)} +``` + +This rule shows a shortcut in that two changes to working memory that +involve the same identifier can be written without repeating the +identifier. + +**VisualSoar** – Once again, put the application of empty in the same +file that holds the proposal of empty. + +The pour operator is more complex because there are two different +situations. One situation is where pouring the water into the jug being +filled can hold all of the water being poured in, such as pouring from +the three gallon jug into an empty five gallon jug. The other situation +is where the jug being filled cannot hold all of the water, so that some +water is left in the source jug, such as when pouring from the full five +gallon jug into an empty three gallon jug. As a result, we have to write +two rules that cover these two situations. + +``` +water-jug*apply*pour*will-empty-empty-jug +``` + +If the task is water-jug and the pour operator is selected, +and the contents of the jug being emptied are less than or equal to +the empty amount of the jug being filled, +then set the contents of the jug being emptied to 0; +set the contents of the jug being filled to the sum of the two jugs. + +```Soar +sp {water-jug*apply*pour\*will-empty-empty-jug +(state ^name water-jug +^operator ) +( ^name pour +^empty-jug +^fill-jug ) +( ^volume +^contents +^empty ) +( ^volume +^contents { <= }) +--> +( ^contents 0 +^contents -) +( ^contents (+ ) +^contents -)} +``` + +**VisualSoar** – Put this rule, along with the next one, into the file +containing the proposal for pour. + +This rule shows why it is useful to have the ^empty augmentation on the +jugs. Without it, it would be very difficult to have the test that the +jug being filled could hold the water from the jug being poured from. + +``` +water-jug*apply*pour*will-not-empty-empty-jug +``` + +If the task is water-jug and the pour operator is selected, and +the contents of the jug being emptied are greater than the empty amount +of the jug being filled, +then set the contents of the jug being emptied to its contents minus the +empty of the jug being filled; +set the contents of the jug filled to its volume. + +```Soar +sp {water-jug*apply*pour*will-not-empty-empty-jug +(state ^name water-jug +^operator ) +( ^name pour +^empty-jug +^fill-jug ) +( ^volume +^contents { > }) +( ^volume +^contents +^empty ) +--> +( ^contents (- ) +^contents -) +( ^contents +^contents -)} +``` + +Now that you have written both operator proposals and application rules, +you can load them into Soar and try them out. It may be difficult to +follow the problem solving, so before running your program you should +probably add the monitoring rules described in the next section. + +## State and Operator Monitoring + +Monitoring rules are useful for printing out the details of the operator +being applied and the contents of each state. Below are four rules that +monitor the selected operator and the state (one rule for each operator +and one for the state). One of the nice things about Soar is that all of +the monitoring rules fire in parallel with the other rules and don’t +interfere with the problem solving at all. + +The write command concatenates all of its arguments, which can include +constants and variables. To make the output readable, you want to have a +linefeed before writing out any text, using the (crlf) command, which +stands for +carriage-return +and +linefeed. + +```Soar +sp {water-jug\*monitor\*state +(state ^name water-jug +^jug ) +( ^volume 3 ^contents ) +( ^volume 5 ^contents ) +--> +(write (crlf) | 3:| | 5:| )} + +sp {water-jug\*monitor\*operator-application*empty +(state ^name water-jug +^operator ) +( ^name empty +^empty-jug.volume ) +--> +(write | EMPTY(| |)|)} + +sp {water-jug\*monitor\*operator-application*fill +(state ^name water-jug +^operator ) +( ^name fill +^fill-jug.volume ) +--> +(write | FILL(| |)|)} + +sp {water-jug\*monitor\*operator-application*pour +(state ^name water-jug +^operator ) +( ^name pour +^empty-jug +^fill-jug ) +( ^volume ^contents ) +( ^volume ^contents ) +--> +(write | POUR(| |:| |,| |:| |)|)} +``` + +With these rules, your program will apply operators and pour water. At +some point it might even reach the desired state; however, it will not +recognize that state and it will just keep going. + +**VisualSoar** – The logical place to add these rules is in a new file +called “monitor” in the elaborations folder. + +### Desired State Recognition + +The final step in creating a program that not only solves Water Jug, but +knows that it solved it is generating a rule that recognizes when a +desired state has been achieved. You need to write a rule that +recognizes when the three-gallon jug has one gallon in it. The action of +the rule should be to print out a message that the problem has been +solved and halt. Write an English version of this rule. + +``` +water-jug*detect*goal\*achieved +``` + +If the task is water-jug and there is a jug with volume three and +contents one, write that the problem has been solved and halt. + +Translating this into Soar is relatively straightforward. + +```Soar +sp {water-jug*detect*goal\*achieved +(state ^name water-jug +^jug ) +( ^volume 3 +^contents 1) +--> +(write (crlf) |The problem has been solved.|) +(halt)} +``` + +Another approach that is often used is to create a representation of the +desired state in working memory and write a rule that compares that +desired state to the current state. There are two advantages to this +approach. First, if you want to try to solve many different Water Jug +problems, a new desired state can be attempted by modifying working +memory (possibly by getting input from a sensor) instead of changing a +rule. Second, in many problems, the description of the desired state can +be used to guide the search using a technique called _means-ends +analysis_. Using means-ends analysis in Soar will be included in a +future tutorial. + +Below is an example rule that creates the desired state, which in this +case is the three-gallon jug containing 1 gallon. This rule will fire +when the initialization operator is selected, in parallel with the other +initialization rule. + +```Soar +sp {water-jug*apply*initialize\*create\*desired-state +(state ^operator.name initialize-water-jug) +--> +( ^desired.jug ) +( ^volume 3 +^contents 1)} +``` + +Below is the modified goal recognition rule. It compares the contents +and volume of the desired jug to one in the current state. This rule is +correct only for desired states that describe a single jug. If the +desired state required specific amounts in each of the two jugs, a more +elaborate rule would be required. + +```Soar +sp {water-jug*detect*goal\*achieved +(state ^name water-jug +^desired.jug +^jug ) +( ^volume ^contents ) +( ^volume ^contents ) +--> +(write (crlf) |The problem has been solved.|) +(halt)} +``` + +If you add these to the earlier rules, the program will halt; however, +it can take a long time. When I ran it ten times, it took an average of +decisions to solve the problem, with a range of 4 (the optimal) to + +**VisualSoar** – There were two different ways of writing the rule(s) +that detects the desired-state. In each case, the purpose of the rule +named water-jug*detect*goal\*achieved is to continually _monitor_ the +state to see if the goal has been reached. Because the purpose of this +rule is to monitor, it makes sense to create a new file (possibly named +“goal-test”) in the elaborations folder and enter it there. If you +chose to go with the second way of writing the goal detection, then +where should water-jug*apply*initialize\*create\*desired-state be +placed? Since it tests the initialize-water-jug operator, it naturally +should be added to the folder that holds that operator. Remember, how +you group your rules is completely up to you. It makes sense to think of +grouping in VisualSoar as an advanced way to comment code: it doesn’t +affect the way the code is interpreted by the computer, but if you or +anyone else ever has to alter the code, it will make it much easier to +understand. From here on out, it is up to you to choose how you want to +group your code. + +### Search Control + +In order to make the search more efficient; you need to add rules that +prefer operators that have the best chance of leading to one of the +desired states. There are few general heuristics that you can use in the +water jug. The total number of possible states is only 16; however, they +are highly connected, and it is difficult to avoid revisiting the same +state over and over again unless a list of visited states is maintained. +Maintaining such a list is possible in Soar, but it is not easy – it +would require creating a copy of every state after an operator has +applied and then comparing a new state to the states in the list. In the +section on planning you will learn about an alternative approach that is +a more natural way for Soar programs to avoid repeated visits to the +same state. For now, we will concentrate on avoiding undoing the last +operator that was applied, such as emptying a jug right after it has +been filled, filling a jug after it has been emptied, or pouring water +from one jug into the other right after the opposite pouring has been +done. These heuristics will be even more effective when for the +Missionaries and Cannibals problem and planning, but for now they allow +us to look at how to maintain a history of the last operator +application. + +In order to avoid undoing the last operator, the program must remember +the operator in the state after it applies. In Soar, this memory is not +automatic. The selected operator is retracted as soon as it applies. In +order to have a record of the previous operator; you must add some rules +that deliberately record the operator each time one is applied. The +rules will be part of the operator application because they will test +the operator in order to record it, which in turn will make the record +persistent (which is what you want). + +Recording an operator has two parts. The first is creating a structure +on the state that is the memory of the most recent operator. The second +is removing any record of an older operator. Given the representation of +the water jug operators in working memory, you will have to write three +rules to record the last operator – one for each operator. If all of the +operators had exactly the same augmentations, then it would be possible +to do this with one rule, and we could change the operator +representations to make this easy, but for now, we will go with the +existing operator representations. The action of these rules should be +to create an augmentation of the state that includes the augmentation of +the selected operator (name, fill-jug, empty-jug as appropriate). It +should not create a link to the original operator because all of the +substructure of that operator will be removed as soon as the rule that +created operator retracts. Try to write English versions of these rules. +We will show only rules for the pour operator. + +``` +water-jug*apply*operator*record*last-operator*pour +``` + +If the task is water-jug and the pour operator is selected, then create +an augmentation of the state (last-operator) with the name of the +operator and a copy of the augmentations augmentation. + +This can then be converted into a Soar rule: + +```Soar +sp {water-jug*apply*operator*record*last-operator*pour +(state ^name water-jug +^operator ) +( ^name pour +^fill-jug +^empty-jug ) +--> +( ^last-operator ) +( ^name pour +^fill-jug +^empty-jug )} +``` + +The rule to remove old records has to test only if the name of the +current operator and the jug augmentation are different because it is +not possible to apply an operator twice in a row. + +``` +water-jug*apply*operator*remove*old*last-operator*pour +``` + +If the task is water-jug and a pour operator is selected and +last-operator does not have the same name and fill-jug, then remove the +last-operator. + +```Soar +sp {water-jug*apply*operator*remove*last-operator*pour +(state ^name water-jug +^operator +^last-operator ) +( ^name pour +^fill-jug +^empty-jug ) + +-( ^name pour +^fill-jug +^empty-jug ) +--> +( ^last-operator -)} +``` + +Once you add these rules, add rules that avoid applying an operator that +undoes the previous operator +(you should write these yourself). + +``` +water-jug*select*operator*avoid*inverse*fill +``` + +If the task is water-jug and the last operator is empty then avoid a +fill. + +``` +water-jug*select*operator*avoid*inverse*empty +``` + +If the task is water-jug and the last operator is fill then avoid an +empty. + +``` +water-jug*select*operator*avoid*inverse*pour +``` + +If the task is water-jug and the last operator is pour from one jug then +avoid a pour the opposite way. + +````Soar +sp {water-jug*select*operator*avoid*inverse*fill +(state ^name water-jug +^operator + +^last-operator ) +( ^name fill ^fill-jug ) +( ^name empty ^empty-jug ) +--> +( ^operator <)} + +```Soar +sp {water-jug*select*operator*avoid*inverse*empty +(state ^name water-jug +^operator + +^last-operator ) +( ^name empty ^empty-jug ) +( ^name fill ^fill-jug ) +--> +( ^operator <)} + +```Soar +sp {water-jug*select*avoid*inverse*pour +(state ^name water-jug +^operator + +^last-operator ) +( ^name pour ^fill-jug ) +( ^name pour ^empty-jug ) +--> +( ^operator <)} +```` + +Adding these rules, should shorten your search on average. My ten-run +average went from to 108. + +### Water Jug Problem Space + +Given a specific problem space, many different problems can be defined +by selecting different initial and desired states. For example, you +could define the problem of starting with both jugs full and getting +four gallons of water into the five-gallon jug. Should that problem be +easier or harder than the original problem? The difficulty can also vary +with the definition of the problem space. The original problem becomes +easy if we expand the problem space to include a jug that holds four +gallons of water (changing the objects in the space), and it becomes +impossible if there is no pour operator. + +One way to understand the Water Jug problems better is to visualize the +complete problem space. The diagram below shows all the states and +operator transitions in the Water Jug problem space. Each state contains +the contents of the five-gallon and three-gallon jugs in order. The +operator transitions are color coded. The goal states are shared in +light green. In this problem, there are two distinct paths to the +solution, one path much shorter (4 steps) than the other path (10 +steps). The difficulty of the problem stems from the fact that there are +usually four operators that can apply to each state but only one leads +toward the solution. The remaining operators lead back toward one of +four states, all of which are at least three steps from the solution. +Therefore it is unlikely that this problem will be solved quickly via a +random search. + +Although we can layout the complete problem space and the connections +between states, the problem solver usually doesn’t have that information +available for examination – it only has the information available in a +single state. For the purposes of this tutorial, we assumed that the +agent starts with only an internal description of the initial state, the +set of legal operators, and the desired state so that the problem will +be solved by starting from the initial state and then searching for a +desired state by selecting and applying operators. At any time the agent +is at only one state and is faced with selecting an operator to apply to +the state to move to a new state. Often AI planners reformulate problems +so that they are searching through a space of plans (and not states of +the problem). In those cases, the states are partial plans and the +operators modify the plan by adding, removing, or reordering plan steps. +For the problems we consider in this tutorial, the problem solver will +always be in the space of states of the problem (task states) and not in +a planning space (although you could use Soar to work in the planning +space, but it would complicate the description of Soar). + +### Summary of Problem Formulation in Soar + +Below is a summary of the main steps in formulating and representing a +problem in Soar. + +1. The state representation. These are the attributes and values that + are used to describe the different states of the problem. For this + problem the state must represent how much water is in each jug. + +2. The initial state creation. An operator will generate the state + where the problem solving starts. In this problem, the initial state + has both jugs empty. + +3. State elaboration rules. These rules elaborate the state with + additional structures that aren’t fundamental to the state (they + aren’t created and deleted by operator application rules), but are + derived from the core aspect of the state. Thus, they are + entailments that are useful abstractions, often making it possible + to create simpler rules for proposing and comparing operators. + +4. The operator proposal rules. These are the rules that propose the + legal state transformations that can be made toward solving the + problem. For this problem there are three classes of operators: + + 1. Pour water from the well into a jug. + 2. Pour water from a jug to a jug. + 3. Pour water from a jug to the well. + +5. The operator application rules. These are the rules that transform + the state when an operator is selected. + +6. The operator and state monitoring rules. These are optional rules + that print out the operator as it applies and prints out the current + state. + +7. The desired state recognition rule. This is a rule that notices when + one of the desired states is achieved. In this problem, the desired + states have one gallon of water in the three-gallon jug. + +8. The search control rules. These are optional rules that prefer the + selection of one operator over another. Their purpose is to avoid + useless operators and/or direct the search toward the desired state. + Theoretically you could encode enough rules so that the correct + operator is always selected for each state. However, you would have + had to already solved the problem yourself to figure out those + rules. Our goal is to have the program solve the problem, using only + knowledge available from the problem statement and possibly some + general knowledge about problem solving. Therefore, search control + will be restricted to general problem solving heuristics. + +9. S1 will always be the identifier of the first state, but that is an + artifact of the way Soar is implemented. diff --git a/docs/tutorials/soar_tutorial/02.md b/docs/tutorials/soar_tutorial/02.md new file mode 100644 index 00000000..a3ae4d6e --- /dev/null +++ b/docs/tutorials/soar_tutorial/02.md @@ -0,0 +1,2309 @@ +# Part II: Simple External Interaction + +## The Eaters Game + +The game used in this part is called “Eaters.” In Eaters, PACMAN-like +eaters compete to consume food in a simple grid world. Below is the +Eaters Playing Board near the beginning of a game with four eaters. + +![](Images/02/image1.jpeg) + +The Eaters world consists of a rectangular grid, 15 squares wide by 15 +squares high. Walls bound all four sides. Interior wall sections are +randomly generated for each new game. No two walls will touch, so there +are no corners, except for exterior walls and no “dead ends” anywhere on +the board. Each eater starts at a random location. Food pellets are in +all other squares of the grid. There are two kinds of food: normal food +(blue circles and worth 5 points) and bonus food (red squares and worth +10 points). An eater consumes food by moving into a square. When an +eater moves out of a square it will be empty (unless another eater moves +into it). + +An eater can sense the contents of cells (eater, normal food, bonus +food, and empty) up to 2 squares away in all directions. On each turn, +an eater can move one square north, south, east, or west. An eater can +also jump two squares north, south, east, or west. An eater can jump +over a wall or another eater. An eater does not consume any food in the +space it jumps over. A jump costs the eater 5 points. + +Whenever two eaters try to occupy the same cell at the same time, they +collide. As a result of the collision, their scores are averaged and +they are teleported to new, random locations on the board. + +### Creating an Eater + +Start the Eaters environment: + +Open the directory where you installed Eaters and launch the appropriate +script file (Eaters.bat on Windows, Eaters.sh on Mac or Linux) + +Once Eaters has started, you will have a new window on your computer +screen. The configuration of the playing board, or map, may look +different than shown. You will use this window to create and destroyer +eaters and run the game. + +![](Images/02/image2.png) + +Individual Eaters are controlled by Soar programs, which are +automatically loaded when an eater is created. Press the “New” button in +the “Agents” area of the Eaters game. A dialog will pop up. + +![](Images/02/image3.png) + +The first thing you must do is select the productions that will define +the behavior of the Eater. Pressing the button “Soar” will pop up a file +selection box: + +![](Images/02/image4.png) + +Select the “Agents\\Eaters\\Tutorial” directory with the mouse, and then +Choose “Open” (or dbl-click on “Tutorial”). The dialog will display the +Soar production files that will be used in this tutorial. Each set of +production rules defines specific behaviors for an Eater. Select the +file “move-to-food.soar” + +![](Images/02/image5.png) + +After you have selected the move-to-food eater, the Create Agent dialog +should look as follows: + +![](Images/02/image6.png) + +The productions in the file “move-to-food.soar” will now be loaded +whenever the “Create Agent” button is pressed. The production set can be +changed before creating another agent by pressing the “Soar” button to +choose a different file. + +You can also pick a color for your eater by clicking on the button to +the right of “Color:” labeled “red”. The color does not affect on the +eater’s behavior, so pick your favorite color. Once you have selected +the productions file, and a color, you are ready to create your eater by +clicking on the Create Agent button. + +![](Images/02/image7.png) + +After clicking on Create Agent, the agent’s Soar Debugger window will +appear. Arrange the Java Eaters window and the Soar Debugger windows so +that they do not overlap. You might need to resize the Soar Debugger +window to make it shorter. Don’t make it too narrow or it will be +difficult to view Soar’s output. + +The Soar Debugger Window will look as shown above, although it may be +larger, and have additional panes. The eater is referred to by its +color, which is listed in the window title (remote red). Once you are +familiar with the Debugger, you can completely customize its appearance. +Across the top are a series of pull down menus, which are described +later. On the left side of the window is the Interaction Window where +trace and debugging information will be displayed. Just below it is the +Command Box where you can type in commands. The bottom of the window +contains buttons for single stepping, running, or stopping the eater, +setting Watch levels, and printing information. The layout of the +buttons may change depending on the size of the window and user +preferences. Features available in the Debugger will be described +throughout this document as they are needed. More details are provided +in “Intro to the Soar Debugger in Java.doc” at +. + +You should also see that the Java Eaters window has changed. The Red +Eater is shown on the map, and the Eater name and score are shown in the +Agent List. To the right of the Agent List is the sensor data for the +Eater, showing the contents of the map cells that surround the Eater. + +![](Images/02/image8.jpeg) + +### Running an Eater + +Your eater is now ready to start eating. Click the Run button on either +the Eater Control Panel (highlighted above) or the Soar Debugger Window. +The eater will start to move and consume food. After a few moves, click +the Stop button on either the Eater Control Panel (next to the “Run” +button) or the Interaction Window. + +At this point, the Eaters Playing Board will look something like the one +below. Your board will look different because the walls and eater +starting position are different each time the game is played. + +![](Images/02/image9.png) + +During the run, the Eaters Sensor window will change with each move, +showing the food pellets, walls, and empty squares that the eater +senses. As shown above, an eater can sense a 5x5 grid of cells, although +the _move-to-food_ rules consider only the four neighboring cells +(north, south, east and west). + +The Interaction window (shown below) displays a trace of the eater’s +decisions. + +![](Images/02/image10.png) + +The line starting with 1: O: O2 shows the first selection of the +move-to-food _operator_ This is followed by successive selections of +move-to-food operators*.* The move-to-food operator causes the eater to +move to a new cell. Some of the eaters will also jump; others will halt +when they cannot sense any food. + +If the trace gets longer than the window can hold, the printing will +scroll up. You can look at decisions that scroll off the window by using +the scroll bar to the right of the window. You can search for +information in the trace by using the find function that is found under +Edit at the top of the Interaction window. + +In Soar, a program consists of rules, and move-to-food has only three +simple rules that move the eater to a neighboring cell that contains +food. As your eaters get more sophisticated, more rules will be loaded. +If you let the eater run for a long time, it will eventually get to a +place where there is no food directly next to it. The move-to-food eater +does not have any rules to respond to this situation (although you could +easily add some) so the eater will stop moving. The trace will look as +follows: + +![](Images/02/image11.png) + +When an eater does not know how to respond to a situation, it will start +generating new states, such as S6, S7, S8, … in the above trace above. +In later sections of the tutorial you will learn how to write rules that +take advantage of these new states, but for now you just need to know +that when they arise, the eater does not know what to do. + +At anytime you can create more eaters. You can create additional eaters +from the same or different files, so that you can create a set of +identical eaters (except for color), or sets of different eaters. Each +time a new eater is created, a new Interaction Window is created, and +the Eaters Agent List is expanded to include the new eater. To make a +new agent with the same set of rules as an existing agent, you can use +the “Clone” button. To create an eater with a different set of rules, +press “New” and use the dialog to select the desired rule set just as +you did when creating your first move-to-food eater. + +![](Images/02/image12.jpeg)From +the Agent List, you can select which eater’s sensor data is displayed by +selecting the eater color with the mouse. When an eater is highlighted, +it can also be “cloned” or destroyed. To destroy an eater, click on the +eater Name in the Agent List (highlight it) and press the “Destroy” +button. + +To reload the production rules for the eater, you should use the reload +button (not implemented yet\!), or if you create an Eater using +VisualSoar you can use the runtime tab to reload the agent, reload a +file, or change a single rule. Reloading is useful if you have found a +bug, modified the rules in the file, and then wish to immediately test +them on the current situation. Soar will replace existing rules with new +versions (a “\#” is printed in the Interaction Window whenever a rule is +replaced), as well as adding any new rules. This can be done at any time +as Soar will match new and modified rules against working memory. + +![](Images/02/image13.jpeg) + +The upper right portion of the JavaEaters window has options for +controlling the Eaters “Simulation.” + +![](Images/02/image14.png) + +Spend some time creating, running and destroying different eaters. +Notice how the eaters move around the board differently, some being much +more efficient at consuming food. + +## Building a Simple Eater Using Rules + +In contrast to the Water Jug, an Eater does not have to initialize the +state with an operator – the Eater will start with information about its +situation coming in through a structure called the input-link. We will +keep the Eater very simple, so all we will do initially is create +operators, which means creating operator proposal and application rules. + +The first operator you will create moves an eater north one step. (It +might not even do that if there is a wall to its north when it is +created.) + +### English Version + +Here is one possible version of the move-north operator. + +```Soar +# Propose*move-north: +# If I exist, then propose the move-north operator. +# Apply*move-north: +# If the move-north operator is selected, then generate an output command to +# move north. +``` + +To write the Soar rule for apply\*move-north, you have to know how to +get an eater to move. All external actions are issued by creating +working memory elements that are augmentations of the output-link. The +output-link is an augmentation of the io object, which in turn is an +augmentation of the state. In Eaters, a move command is issued by +creating a “move” augmentation on the output-link object, which in turn +has an augmentation called “direction” with a value of the direction to +move: “north”, “south”, “east”, or “west”. For each task in Soar, a set +of output commands is defined, and in Eaters there are two commands: +move and jump. + +You should now try to write the Soar rules for the move-north operator. + +To create your own agent for eaters, use your favorite text editor, and +save the file that you create with a .soar file extension. To create an +agent that loads the production that you have written, create an agent +as in section 1.1, but this time instead of loading “move-to-food.soar”, +load the file that you just created. + +## Soar Version + +Below is the proposal rule I wrote. You can find this rule in the file +“move-north.soar” in the “Agents\\ \\Eaters\\Tutorial” directory where +you found “move-to-food.soar”. The proposal rule is exactly the same as +the proposal rule for hello-world, except that the names of the rule and +the operator are changed. + +```Soar +sp {propose*move-north +(state ^type state) +--> +( ^operator +) +( ^name move-north)} +``` + +The rule that applies the operator has more differences. Its action is +to add a move command to the output-link. This command will cause the +eater to move one cell to the north. In order to add the command to the +output-link, the rule must match the output-link identifier in its +conditions with a variable. Therefore, conditions are included that +match the io () object, and then the output-link (). + +```Soar +sp {apply*move-north +(state ^operator +^io ) +( ^output-link ) +( ^name move-north) +--> +( ^move ) +( ^direction north)} +``` + +The exact order of the conditions (and actions) does not matter, except +that the first condition must match the state identifier. + +You should change your rules if they were significantly different than +these. Then create a move-north eater and type “watch 3” on the command +line so that you can watch the productions fire. Run the eater by +clicking on the “Step” button until your eater moves north. If your +eater doesn’t seem to be working, skip ahead to the Debugging Your Eater +section. Once your eater has moved north, your interaction window should +look like the following: + +![](Images/02/image15.png) + +But what happened to your eater? After selecting move-north and taking +one step, the eater had nothing more to do and a new state is created +(s6 in the above Figure). Unfortunately, the rules you wrote are not +sufficient to get the eater to continually move north. In the next +section, you will modify your eater so that it continues to move north. + +### Shortcuts + +The apply\*move-north rule has lots of variables in it whose sole +purpose is to make connections between attributes. For example, is +used to later match the name of the operator and is used to later +match the output-link. Similarly, is used in the action to +connect the output-link to the final direction. None of these variables +is used in both conditions and the actions. + +```Soar +sp {apply*move-north +(state ^operator +^io ) +( ^output-link ) +( ^name move-north) +--> +( ^move ) +( ^direction north)} +``` + +To simplify the writing and reading of rules, Soar allows you to combine +conditions that are linked by variables. To do this, you can just string +together attributes, replacing the intermediate variables with a period, +“.”, to separate the attributes. In Soar, we call this “dot” or “path” +notation. Below is the same rule as before using dot notation. + +```Soar +sp {apply*move-north +(state ^operator.name move-north +^io.output-link ) +( ^move.direction north)} +``` + +This rule is exactly the same as the original from Soar’s perspective. + +One mistake you want to avoid making is to use dot notation in the +action when you are creating multiple sub-attributes of a new object. +This will create multiple objects, each with a single sub-attribute. For +example, if you want create a second augmentation of the move object +called speed (which is irrelevant in Eaters), you _do not_ want to do +the following: + +```Soar +--> +( ^move.direction north + ^move.speed fast) +``` + +This is equivalent to: + +```Soar +--> +( ^move + ^move ) +( ^speed fast) +( ^direction north) +``` + +These actions will create _two_ move augmentations on the output-link, +each with a single attribute. The correct action is: + +```Soar +--> +( ^move ) +( ^speed fast + ^direction north) +``` + +## Move-North Operator: Multiple Moves + +Although the move-north operator did move one step north, the eater +never takes a second step. In this section, you are going to find out +why it takes only one step and modify the eater so that it can take +multiple steps. This is one of the most complex sections of the +tutorial, so study it carefully. + +### Operator Selection: Multiple Instances + +In Soar, each action in the world, such as moving an eater, should be +performed by a separate operator _instance_. An operator instance is a +separate operator in working memory element created by the firing of an +operator proposal rule. Thus, each action should test for the creation +of a new operator object in working memory. Different instances of the +same operator will have the same name, and sometimes will have the same +augmentations, but a given instance will be used for only one move. Some +operator instances may include many actions, but they will be selected +and applied only once. + +Therefore, new instances of the move-north operator should be created in +working memory for each new move. You should not attempt to have the +move-north operator selected once, and have it move an eater multiple +times. Instead, you should design you eater so that a new instance of +the move-north operator is created for each move. You can do this by +having the operator proposal rule fire each time the eater is to move. +How can you change propose\*move-north so that it fires after each move? +As the rule is currently written, it will fire only once because it only +tests ^type state, which stays in working memory forever. Your rule +needs to test working memory elements that change each time the eater +moves – those working memory elements that correspond to the eater’s +senses on the input-link. Soar is designed so that changes to the +input-link are made following output, just in time to affect operator +proposals. + +The information that comes in on the input-link consists of objects with +attributes and values. + +``` +(I2 ^eater I4 ^my-location I5) +(I4 ^direction south ^name red ^score 25 ^x 1 ^y 10) +``` + +The input-link object, I2, has two augmentations. The first, ^eater, has +information about the eater: its current direction, its name, its +current score, and its x, y coordinates. The second, ^my-location, has +additional substructure (not shown) that includes the eater’s sensing of +nearby cells. Some of the ^eater information changes during the game: + +- The x location will change when the eater moves east or west. +- The y location will change when the eater moves north or south. + +Thus, whenever the eater moves, either the x or y location will change. +We can modify the conditions of our proposal rule to test both of these +working memory elements, and eliminate the test for ^type state. + +```Soar +sp {propose*move-north +(state ^io.input-link.eater ) +( ^x ^y ) +--> +( ^operator +) +( ^name move-north)} +``` + +What will happen? First, when the original working memory elements for x +and y are removed from working memory, the original move-north operator +will be removed from working memory because the rule instantiation that +created it no longer matches – some of the working memory elements +responsible for the match are gone. We call this _retracting_ the rule +firing. Rules such as propose\*move-north maintain their actions in +working memory only as long as they match exactly the same working +memory elements. + +Second, a new instance of the move-north operator will be created +because propose\*move-north will match the new values of x and y and +fire. To see these changes, modify your move-north operator as above and +then rerun it. This time, we are going to increase the tracing so that +we can see the individual changes to working memory. To do this, type +“watch 4 --timetags” in the interaction window. + +The figure below shows the end of the trace. The trace is long because +it includes the addition and removal of all of the sensory data that +changes during a move (not shown below). In the trace, an addition to +working memory is shown preceded by an “=>”, while a removal from +working memory is preceded by an “<=”. Each working memory element also +contains a number, such as 119 for the top element in the trace below. +This is the _timetag_ of the working memory element – a unique number +generated when a working element is created. Soar sometimes displays +only timetags instead of the full working memory element, and the +timetag can be used in the print and wmes commands for displaying the +full working memory element (more details later). + +![](Images/02/image16.jpeg) + +Your eater will now move north a second time (and a third, etc, if not +blocked) because the proposal matches against a new set of x and y +coordinates, apply\*move fires again, and a second move command is +created. But there are still problems. Step the eater a few more times +and then choose the “output” tab on the upper right section of the eater +Debugger window to see the eater’s ^output-link. You will notice that +the original move command (I3 ^move M2) is not removed from the +output-link, and the output-link will gradually accumulate stale +commands. The old move command is not removed because it is the action +of an operator application rule. Such actions are not automatically +removed when the rule no longer matches. + +### Operator Application: Persistence + +As noted above, proposing and selecting a second operator makes the +eater move a second time, but the accumulation of more and more move +commands will eventually become a problem. Why aren’t these commands +automatically removed like the preferences for operators? The reason is +that the rule that creates them is part of the application of an +operator, and operator applications create _persistent_ working memory +elements. + +Persistence is necessary for creating memories of prior events, such as +the memory of something sensed in the environment. For example, if an +eater needed to remember the location of uneaten food so that it could +return to it later, the eater would have to create a persistent +structure in its working memory. Otherwise, as soon as it stopped +sensing the food, it would forget about it. In Soar, all working memory +elements created by an operator application rule are persistent. A rule +is an operator application rule if it tests a selected operator and +modifies the state. Persistent working memory elements are said to be +_operator-supported_, or _o-supported_, because they are created by +operators. For example, apply\*move-north is an operator application +rule and creates o-supported working memory elements. + +```Soar +sp {apply*move-north +(state ^operator.name move-north +^io.output-link ) +--> +( ^move.direction north)} +``` + +Just as it is important to have persistent memories, it is also +important to have structures that are removed automatically when the +reason for creating them goes away. Actions of rules that test only the +state, or that test the operator and elaborate only the operator create +non-persistent preferences or working memory elements. Obvious examples +of non-persistent structures are the preferences for operators. These +are created by operator proposal rules, which retract the operators when +the proposal conditions change. Other examples include state elaboration +rules and operator elaboration rules. Non-persistent working memory +elements and preferences are called _instantiation-supported,_ or +_i-supported_, because they only persist as long as the rule +instantiation that created them. For example, propose*move-north is not +an operator application rule because it does not test a selected +operator. All of its actions are i-supported and they will be removed +from working memory when propose*move-north no longer matches. + +```Soar +sp {propose*move-north +(state ^io.input-link.eater ) +( ^x ^y ) +--> +( ^operator +) +( ^name move-north)} +``` + +Returning to the problem of moving the eater, you need to add a rule +that removes old move commands from the output-link after the move is +finished. In Eaters, the output system creates an augmentation on the +move object after the action has executed: ^status complete. To remove a +structure from working memory, a rule “rejects” a working memory element +in its action. A reject must be part of an operator application rule +because it is a persistent change to working memory. Therefore, the +removal must test the move-north operator so that it is an operator +application rule. In English, the rule is: + +```Soar +# Apply*move-north*remove-move: +# If the move-north operator is selected, +# and there is a completed move command on the output link, +# then remove that command. +``` + +In Soar, the action of a rule can remove a working memory element by +following it by a reject preference, which is designated with a minus +sign (“-“). The above rule is translated in to Soar as follows: + +```Soar +sp {apply*move-north*remove-move +(state ^operator.name move-north +^io.output-link ) +( ^move ) +( ^status complete) +--> +( ^move -)} +``` + +The action of this rule removes the working memory element specified by +`( ^move )`, which would be something like `(i3 ^move m1)`. +When this working memory element is removed, all of the augmentations of +m1 are automatically removed because they are no longer _linked_ to the +rest of working memory. In this case, `(m1 ^direction north)` and +`(m1 ^status complete)` are removed. These would not be removed if they +remained linked via another working memory element that had m1 as a +value. + +This is an operator application rule, so it will not fire during the +following operator proposal phase. Instead, it will fire during the next +operator application phase, after a new move-north operator has been +selected. It will not interfere with other operator application rules +because rules fire (and retract) in _parallel_ as a wave. The +parallelism is only simulated, but the effect is as if all the rules +fire or retract at the same time. Once a wave of rules has fired, their +actions may cause additional rules to match or retract. Soar will +continue to fire or retract additional waves of rules until no more +rules match. Thus, within the phases of proposing and applying +operators, Soar fires all rules that match until _quiescence_, that is, +until no more rules match. During the propose phase, only rules that +have i-supported actions will fire. During the apply phase, both +o-supported and i-supported rules fire. + +Returning to our diagram of the phases of Soar, both the propose +operator and apply operator phases can be expanded to show that multiple +rules fire and retract in parallel until quiescence. + +### Running and Tracing + +When running your new eater, it will go north until it hits a wall or +the border. When it can no longer move, it will start generating states +because it cannot successfully apply and terminate the move-north +operator. Below is a sample trace, with the “watch 3 --timetags” in use, +of the interaction window for the first two moves to the north by an +eater. + +![](Images/02/image17.png) + +If you look closely at this trace, you see that two rules are firing at +the same time: apply*move-north, and apply*move-north\*remove-move. +One is adding the new move command to the output-link at the same time +that the other is removing the previous move command. There are also +examples of rules firing and retracting at the same time where an +operator is being removed from working memory at the same time that a +new operator is being created. + +Instead of letting Soar run free, you can step Soar through each +decision using the “Step” button on either the Soar Debugger or the Java +Eaters window. The default will stop each step before the application +phase, right after Soar has made an operator selection (decision phase). +It’s possible to change the location where Soar will stop when stepping, +using the Soar Cycle widget in the Debugger. + +![](Images/02/image18.jpeg) +![](Images/02/image19.jpeg) + +The red icon in the diagram shows the point where Soar will stop when +the Step button is used. The green icon shows where Soar is in its +execution cycle. Move the Stop Point to the location between “World” and +“Input” as shown above. This is just after the output commands have been +added to the output-link and just before the eaters are about to move. +In the following trace, the Watch level is 1, the step button has been +used, and then the output-link is examined. This is repeated twice. + +![](Images/02/image20.png) + +From prior runs, you know that I3 is the identifier of the output-link. +Although Soar does not guarantee that I3 will always be the identifier +of the output-link, it always is, just as I1 is the identifier of the io +object, I2 is the identifier of the input-link, and S1 is the identifier +of the first state. + +When you print I3, you see that the move command is correctly added to +the output-link. You can see more than one level of augmentation by +using the –d flag on the print command as shown in the upper right +monitoring window. + +You can also step through one phase at a time by using the +run 1 –p command. + +## Move-To-Food Operator + +In this section you will create an eater that greedily moves to any food +it senses. You will use the lessons you learned from the move-north +operator, and learn more about the structure of the input-link and +operator preferences. + +Each eater can sense the food and walls that surround it, two steps in +each direction. The Eaters Info window shows what an eater can sense. +For this eater, you will write an operator that moves the eater to one +of the neighboring cells (north, east, south, or west) that contains +normalfood or bonusfood. Since food can be in more than one of the +neighboring cells, more than one operator may be proposed. Soar does not +automatically select randomly from among a set of proposed operators – +it will get a _tie impasse_ if there are multiple operators with only +acceptable preferences. To avoid ties, Soar has additional preferences. +For this exercise, it does not matter which food an eater consumes +first, so you can use a preference that makes the decision procedure +arbitrarily select one of the proposed operators. + +Based on the above discussion, you will need four rules for the +move-to-food operator. + +- You need one rule to propose the operator when there is normal-food + in a neighboring cell and a second rule to propose the operator when + bonus-food is in a neighboring cell. Unlike the move-north operator, + these proposal rules do not have to test the coordinates of the + eater because the contents of the neighboring cells will change when + the eater moves. The contents will change (the working memory + elements for contents of all of the sensed cells are removed and + re-added to working memory) even if the sensed object is the same + type. The operator proposal rules can also create the indifferent + preferences that lead to a random selection. + +- You need a third rule to apply the operator and move the eater in + the correct direction. + +- You need a fourth rule to remove the move command from the + output-link. + +Now try to write English versions of the move-to-food operator. Use the +English versions of the move-north operator as a guide: + +```Soar + Move-north operator +# Propose*move-north: +# If I am at a specific location, then propose the move-north operator. + +# Apply*move-north: +# If the move-north operator is selected, +# generate an output command to move north. + +# Apply*move-north*remove-move: +# If the move-north operator is selected, +# and there is a completed move command on the output link, +# then remove that command. +``` + +### English Version + +Here is one possible set of rules for the move-to-food operator. + +``` +# Move-to-food operator +# Propose*move-to-food\*normalfood +# If there is normalfood in an adjacent cell, +# propose move-to-food in the direction of that cell +# and indicate that this operator can be selected randomly. +# +# Propose*move-to-food\*bonusfood +# If there is bonusfood in an adjacent cell, +# propose move-to-food in the direction of that cell +# and indicate that this operator can be selected randomly. +# +# Apply*move-to-food +# If the move-to-food operator for a direction is selected, +# generate an output command to move in that direction. +# +# Apply*move-to-food\*remove-move: +# If the move-to-food operator is selected, +# and there is a completed move command on the output link, +# then remove that command. +``` + +Converting these rules to Soar requires a more detailed examination of +preferences and the input-link. + +- Preferences. To select randomly among the proposed operators, Soar + has a preference called _indifferent_. The equal sign, “=”, is used + to signify an indifferent preference for an operator, just as “+” + signifies acceptable. The indifferent preference means that the + decision procedure can randomly select among all operators with + indifferent preferences, so it is important that all proposed + operators have indifferent preferences. Even though we will create + an indifferent preference for these operators, the acceptable + preference is still necessary and an operator will not be selected + if it does not have an acceptable preference. + +- Input-link representation. The input-link has two augmentations: + eater and my-location. Remember that the value of the my-location + augmentation is the eater’s current position and is a cell in middle + of the 5x5 Eaters Info sensory field. On the next page there is a + graphic depiction of this cell and all of the other cells in the + sensory field. Adjacent cells are augmentations labeled north, east, + south, and west. Every cell also has a ^content augmentation, whose + value can be wall, empty, eater, normalfood, or bonusfood. If the + cell has an eater in it, there will be additional augmentations (not + shown in the figure) for the color of the eater (^eater-color), and + the eater’s current score (^eater-score). Below the figure for the + input-link structure is the corresponding Eaters Info window. + +Based on the information in those figures, you should try to write a +Soar rule for propose\*move-to-normalfood. Two hints: + +1. Write down the sequence of attributes that go from the state to the + value of the content of a cell, separating each attribute with a + “.”. + +2. Use a variable to match the direction augmentations to a neighboring + cell and then test that the content is normalfood. That variable in + the attribute position will match any direction augmentation between + cell: north, east, south, or west. You can write it: `^` + +![](Images/02/image21.png) + +### Soar Version + +Below is the proposal rule I wrote without dot notation so that it is +clear which working memory elements are being matched. The proposal rule +tests a sequence of linked identifier-attribute-values, and includes a +variable for the attribute that leads to an adjacent cell. The variable +will match north, east, south, or west if the final condition matches an +adjacent cell containing (has “^content”) normalfood. + +```Soar +sp {propose*move-to-normalfood +(state ^io ) +( ^input-link ) +( ^my-location ) +( ^ ) +( ^content normalfood) +--> +( ^operator +) +( ^operator =) +( ^name move-to-food +^direction )} +``` + +The indifferent preference tells Soar that a random selection can be +made between proposed operators. The `` in the action +augments the operator with the direction of an adjacent cell containing +normalfood. When an eater is surrounded by food, the `` +variable will match all directions, leading to four matches of the rule. +In Soar, all new matches fire in parallel, creating new operators, each +with a different ^direction augmentation. For example, if there was +normalfood to the south and west, two operators would be created, one +with ^direction south and one with ^direction west*.* + +This rule can be written much more concisely using dot notation, and a +short cut for the preferences. + +```Soar +sp {propose*move-to-normalfood +(state ^io.input-link.my-location..content normalfood) +--> +( ^operator + =) +( ^name move-to-food +^direction )} +``` + +The rule that proposes the operator to move to consume bonusfood is very +similar. + +```Soar +sp {propose*move-to-bonusfood +(state ^io.input-link.my-location..content bonusfood) +--> +( ^operator + =) +( ^name move-to-food +^direction )} +``` + +The rule to create the move command on the output link is very similar +to the one used in move-north. The only difference is that instead of +always using “north” as the direction, it uses the direction created by +the operator proposal rule and matched by variable ``. + +```Soar +sp {apply*move-to-food +(state ^io.output-link +^operator ) +( ^name move-to-food +^direction ) +--> +( ^move.direction )} +``` + +The final rule removes the move command from the output-link when it has +completed. + +```Soar +sp {apply*move-to-food\*remove-move +(state ^io.output-link +^operator.name move-to-food) +( ^move ) +( ^status complete) +--> +( ^move -)} +``` + +### Shortcuts and Extensions + +The rules propose*move-to-normalfood and propose*move-to-bonusfood +differ only in the tests for normalfood and bonusfood. Instead of +writing an individual rule for each of those values, it is possible to +write a single rule that tests for any one of a set of alternative +values. The alternative values are written in the same position as a +single value, but are surrounded by double angle brackets: << +normalfood bonusfood >>. Any number of different values can be +included, but none of them can be a variable. Using this notation, the +two rules can be rewritten as the following rule: + +```Soar +sp {propose*move-to-food +(state ^io.input-link.my-location..content << normalfood bonusfood >>) +--> +( ^operator + =) +( ^name move-to-food +^direction )} +``` + +This notation can also be used for testing alternative values of +attributes. + +Soar supports another shortcut, similar in spirit to dot notation that +allows you to eliminate variables in conditions whose sole purpose is to +link conditions. Dot notation works if there is a single augmentation. +However, if there are multiple augmentations, dot notation doesn’t help. +Grouping, using parentheses, handles this case. In place of where a +variable would go as an action, you can insert the augmentations of the +value. For example, the original apply\*move-to-food was: + +```Soar +sp {apply*move-to-food +(state ^io.output-link +^operator ) +( ^name move-to-food +^direction ) +--> +( ^move.direction )} +``` + +This can be replaced by + +```Soar +sp {apply*move-to-food +(state ^io.output-link +^operator (^name move-to-food +^direction )) +--> +( ^move.direction )} +``` + +Although this does eliminate unnecessary variables, it often makes the +rules more difficult to read, so this notation will not be used in the +rest of the tutorial. + +One more thing you might want to add is the ability to trace the +direction of the selected operator. You can do this with a rule that +tests that the operator is selected and uses the write action. + +```Soar +sp {monitor*move-to-food +(state ^operator ) +( ^name move-to-food +^direction ) +--> +(write |Direction: | )} +``` + +This rule will fire in parallel with apply\*move-to-food because it +matches a selected operator. + +### Example Trace + +![](Images/02/image22.png) + +## Debugging Soar Programs + +In trying to write the operators in the previous section, you may have +made some mistakes. In this section, you will learn techniques for +finding and fixing bugs in Soar programs. The techniques are separated +based on the cause of the error: + +- When the rules for an eater are read into Soar, Soar checks to make + sure that the rules are legal and displays messages when it finds + errors. These types of errors are called _syntax errors_. + Visual-Soar will also check for syntax errors in rules. I recommend + using Visual-Soar to check for errors first. + +- If your rules load into Soar without any error messages, there can + still be mistakes in the underlying logic of the rules. These are + _semantic errors_. + +This section starts with a subsection on Syntax Errors. Subsections +follow this on different techniques for monitoring and examining your +program while it is running. The last subsection is a walk through of +using these techniques to debug a program. + +### Syntax Errors + +There are many different types of syntactic errors that you can have in +your programs. The eight most common errors are: + +1. Missing comment character +1. Missing { +1. Missing ) +1. Extra ) +1. Missing } +1. Missing state +1. Missing ^ +1. Disconnected rhs action variable. + +Fortunately, with the development of VisualSoar, locating and fixing +syntax errors in Soar productions has become relatively easy. Before +loading rules into the debugger to execute a Soar program, check for +syntax errors using VisualSoar. To do this, click Datamap | Check All +Productions for Syntax Errors. + +![](Images/02/image23.png) + +VisualSoar will check all of your productions to make sure that they +conform to the standard syntax rules. The results of this check will +appear in the feedback window (which is located at the bottom of the +VisualSoar window). If an error is found, an informative error message +will appear in blue. The first part of the error message will be the +name of the file where the error was found and its line number in the +format (line number): This is then followed by more output +which will include the specific path of the file the error was found in, +and why VisualSoar considers it a syntax error: + +![](Images/02/image24.png) + +One of the best features of VisualSoar is that double-clicking on an +error message allows you to see exactly where the error occurred. +Double-clicking an error message automatically opens the window in which +the error was found, and highlights the illegal area in yellow. For +instance, double-clicking this error message would cause this window to +pop-up: + +![](Images/02/image25.png) + +If no errors are found, “There were no errors detected in this project” +will be printed in the feedback window. + +We suggest playing around with this tool. Delete brackets and braces, \# +signs and -->’s to see what errors VisualSoar gives you so that you are +able to quickly recognize what types of messages certain errors give. +This will make future debugging much easier. + +### Write Statements + +One of the oldest debugging techniques in traditional programming +languages is to add print statements throughout the program. Although +not the most effective technique, it can be easily used in Soar by +adding write statements in the action of rules. To make the output +readable, you want to have a linefeed before writing out any text, using +the (crlf) command, which stands for +carriage-return +and +linefeed. + +For example, if you want to keep track of all of the move-to-food +operators that are proposed, including their direction and expected +food, you could take the original rule given below: + +```Soar +sp {propose*move-to-food +(state ^io.input-link.my-location..content << normalfood bonusfood >>) +--> +( ^operator + =) +( ^name move-to-food +^direction )} +``` + +and modify it by adding a write statement. To get the right values in +the action requires a variable for the food type in the condition. + +```Soar +sp {propose*move-to-food +(state ^io.input-link.my-location..content +{ << normalfood bonusfood >> }) +--> +(write (crlf) | Propose move | |, for | ) +( ^operator + =) +( ^name move-to-food +^direction )} +``` + +The write command concatenates all of its arguments, which can include +constants and variables. + +![](Images/02/image26.png) + +### Runtime Debugging Commands + +While Soar is running, there are many commands you can use to get +information on what the current state of the system is, what happened in +the past, and what is about to happen. This subsection is organized by +the different commands. To learn the details of all of these commands, +you should check the Soar User’s Manual. + +#### print + +You have already seen the print command, which is used to print out +working memory structures. Print can take a variety of arguments. One of +the most useful is to print to a given depth. This prints not only the +current identifier and all of its augmentations, but also all of their +augmentations recursively to a given depth. For example, you can print +out the current state to a depth of 2 by using the following command: + +``` +red> print -–depth 2 s1 +``` + +The order is important and s1 must come at the end of the print +statement. The result is: + +``` +(S1 ^type state ^superstate nil ^io I1 ^operator O2 ^operator O4 + +^operator O3 + ^operator O2 + ^operator O1 +) +(I1 ^input-link I2 ^output-link I3) +(O2 ^name move-to-food ^direction south) +(O4 ^name move-to-food ^direction west) +(O3 ^name move-to-food ^direction north) +(O1 ^name move-to-food ^direction east) +``` + +You can also use the mouse to print working memory objects by right +clicking on an identifier. If you select the print option, you will see +that there a many other options for printing objects in working memory. + +#### wmes + +The wmes command can also be used to print out individual working memory +elements instead of complete objects. This command also prints out the +_timetag_ of an individual working memory element. This is a unique +number that Soar uses to keep track of each working memory element. + +``` +red> wmes o1 +(120: o1 ^name move-to-food) +(121: o1 ^direction east) +``` + +Timetags can also be used as arguments in the print and wmes commands. +Print will display all of the working memory elements that have the +given timetag as their identifier, while wmes will display only the +specified working memory element. + +``` +red> print 120 +(O1 ^name move-to-food ^direction east) +``` + +``` +red> wmes 120 +(120: o1 ^name move-to-food) +``` + +##### matches + +One of the questions you will frequently want to ask is, “What rules are +about to fire?” The matches command will return a list of all rules that +are ready to fire, separated into those that will be operator +applications (O Assertions), those that are will create I-support +augmentations (I Assertions), and those that will remove I-supported +augmentations (Retractions). + +``` +red> matches + +O Assertions: +I Assertions: +Retractions: +``` + +Matches also can be invoked through the “show” item on the menu list, or +by right clicking on the interaction window, then selecting the +production item on the menu that appears. Note; matches will not show +rules that completely match, but have _already fired._ Thus, if you stop +Soar after a rule has fired, matches alone will not show it. + +Matches can also take as an argument the name of a rule. It will then +print out the rule, condition by condition, with information on how many +working memory elements match that condition and are consistent with all +of the variables in the previous conditions. When Soar loads in a rule, +it automatically reorders the conditions of the rule so that it can +efficiently match it, so the ordering of the conditions from matches +will not be the same as the ordering of the rule when you wrote it. + +If a rule completely matches, then it has either already fired, or is +about to fire. + +``` +red> matches propose*move-to-food +1 (state ^io ) +1 ( ^input-link ) +1 ( ^my-location ) +7 ( ^ ) +3 ( ^content { << normalfood bonusfood >> }) +3 complete matches. +``` + +In this example, the first three conditions have a single match, and +then the fourth condition matches seven different working memory +elements. These are all of the augmentations of my-location. The final +condition then restricts the matches to be only those that have content +normalfood or bonusfood. + +If a rule does not completely match, the condition that failed to match +will be preceded by “>>>>” as in: + +``` +red> matches apply*move-to-food\*remove-move +1 (state ^operator ) +1 ( ^name move-to-food) +1 ( ^io ) +1 ( ^output-link ) +1 ( ^move ) +>>>> ( ^status complete) +0 complete matches. +``` + +Matches can also print out working memory elements that match the +conditions by using the -timetags (for just the timetags) or –wmes (for +complete working memory elements) arguments. Matches can be invoked by +clicking with the right mouse button on the name of a production and +then selecting “production” from the menu that appears. + +##### preferences + +There is a special command to print out the preferences for selecting an +operator. This command also works for any working memory element (prior +versions of Soar allowed preferences for all working memory elements and +there are still vestiges of them in the current version). To use the +preference command, you give an identifier and an attribute. Soar will +then print out all of the preferences for all values of that identifier +attribute pair. For example, to print out all of the preferences for the +operator attribute: + +``` +red> preferences s1 operator +Preferences for S1 ^operator: +acceptables: +O7 (move-to-food) + +O8 (move-to-food) + +O9 (move-to-food) + +unary indifferents: +O7 (move-to-food) = +O8 (move-to-food) = +O9 (move-to-food) = +``` + +This example shows that there are three operators proposed (O7, O8, O9), +and each one has an acceptable and indifferent preference. + +The preferences command has another feature that makes it extremely +useful. Using the -name argument, it will tell you the name of the +production that created the preference. For example, if you want to +discover why there is the working memory element (I3 move m3), you can +type: + +``` +red> preferences I3 move --names +Preferences for I3 ^move: +acceptables: +M3 + +From apply*move-to-food +``` + +This tells you that apply*move-to-food creates this working memory +element. If you want to find out what working memory elements matched +apply*move-to-food when it created (I3 move m3), you can type: + +``` +red> preferences I3 move --wmes +Preferences for I3 ^move: +acceptables: +M3 + +From apply*move-to-food +(212: S1 ^operator O8) +(205: O8 ^name move-to-food) +(206: O8 ^direction north) +(3: S1 ^io I1) +(5: I1 ^output-link I3) +``` + +You can also invoke preferences by clicking with the right mouse button +on an attribute of a working memory element in the interaction window. +Select the preferences command from the menu that appears and then +select the appropriate preferences command. + +### Semantic Errors + +In this section you will use the techniques from the prior section to +fix semantic errors. In a rule-based system, semantic errors have three +general results: + +1. A rule does not fire when it should. +1. A rule fires when it shouldn’t. +1. The action of the rule is incorrect. + +Unfortunately, when you have an error, you don’t know which type it is. +Luckily, there are general techniques for finding all types of semantic +errors. + +The first, and most important, step in finding semantic errors is +knowing what you think the program should be doing at each step. Only by +knowing what the program should do, will you be able to detect when it +does something wrong. Usually you will notice that the wrong operator is +selected, or that no operator is selected (a substate is created). + +I’ve created a file called “semantic-errors.soar” in the +“Agents\\Eaters\\Tutorial” directory for move-to-food that has some +bugs. Create an eater with this file and also open the file in a text +editor so you can modify as we go along. You should expect that +propose\*move-to-food to fire during the first step, so be bold and +click on the step button and see what happens. You should get a trace +like: + +``` +red> \*\*\* +0: ==>S: S1 +1: ==>S: S2 (state no-change) +``` + +This clearly indicates that propose*move-to-food did not fire. To find +out why, try matches propose*move-to-food. + +``` +red> matches propose*move-to-food +1 (state ^io ) +1 ( ^input-link ) +1 ( ^my-location ) +7 ( ^ ) +>>>> ( ^contant { << normalfood bonusfood >> }) +0 complete matches. +``` + +There is a problem with the last condition. You can examine working +memory to find out what it should be matching and after some +examination, you should realize that the name of the attribute should be +“content”, not “contant”. Change the rule in the file. If you have been +using Visual-Soar, it would have found this error while you were +creating the Eater. To avoid destroying and creating a new eater, you +can just reload the rules by clicking on the Commands menu of the +Interaction Window and selecting “Reload Productions”. Now you can try +going one step again. You should have the following in your interaction +window: + +``` +red> \*\*\* +0: ==>S: S1 +1: O: O1 (move-to-food) +``` + +At this point, you can check which rules are about to fire by using the +matches command. + +``` +red> matches +O Assertions: +apply*move-to-food +I Assertions: +Retractions: +``` + +That looks fine, so take one more step. Unfortunately, the eater doesn’t +move. What’s the problem? You should examine the output-link to see if +the move command was correctly created: + +``` +red> print --depth 2 i3 +(I3 ^moves M1) +(M1 ^direction east) +``` + +Unfortunately, you have to inspect this structure and realize that the +command it creates is “moves” not “move”. Correct this, reload, and take +another step. Now the eater moves and runs appropriately. + +## Generalized Move Operator + +The move-to-food operator you created in the last section would get +stuck when there was no food in the cells adjacent to the eater. It also +did not prefer bonusfood to normalfood. In this section, you will +generalize the move-to-food operator to be an operator that can move to +a cell with any type of content. Once you have created such a +generalized move operator, we will introduce additional preferences that +allow you to create a greedy eater that never gets stuck. + +### Move Operator Proposal + +The proposal for the move operator needs to test that there is an +adjacent square that it can move into. It should not propose moving into +a wall. There are two possible approaches to write this test. The first +is to test all of the content values that are ok to move into: +normalfood, bonusfood, eater, or empty. The second is to test that the +content does not equal wall. Taking the first approach gives us the +following English version of the proposal: + +```Soar +# Propose*move*1: +# If there is normalfood, bonusfood, eater, or empty in an adjacent cell, +# propose move in the direction of that cell +# and indicate that this operator can be selected randomly. +``` + +It is straightforward to translate this into Soar based on the +move-to-food operator proposal: + +```Soar +sp {propose*move*1 +(state ^io.input-link.my-location..content + +<< empty normalfood bonusfood eater >>) +--> +( ^operator + =) +( ^name move +^direction )} +``` + +Although this is adequate, it will make future selection rules simpler +if the operator also contains the contents of the cell. Thus, an +alternative version is: + +```Soar +# Propose*move*1a: +# If there is normalfood, bonusfood, eater, or empty in an adjacent cell, +# propose move in the direction of that cell, with the cell’s content, +# and indicate that this operator can be selected randomly. +``` + +To translate this into Soar requires matching the cell’s content to a +variable and then using that variable in the action as an augmentation +of the operator, such as `^content `. However, the value is +already matched by << empty normalfood bonusfood eater >>. What is +needed is a way to match both a variable and the list at the same time. +In Soar this is done by surrounding the two (or more) things to match +against the same item with curly braces: “{ }”. Thus, the Soar version +of the proposal becomes: + +```Soar +sp {propose*move*1a +(state ^io.input-link.my-location..content +{ << empty normalfood bonusfood eater >> }) +--> +( ^operator + =) +( ^name move +^direction +^content )} +``` + +Although this rule is adequate, it forces you to list all of the +contents except walls. This rule will have to be changed if we ever add +other types of food (e.g., superbonusfood). It might be better to write +a rule that tests that the content is _not equal_ to wall. This can be +done in Soar by using “<>”. The not equal test can also be combined +with the variable as in propose*move*1a, giving the following rule: + +```Soar +sp {propose*move*2a +(state ^io.input-link.my-location..content +{ <> wall }) +--> +( ^operator + =) +( ^name move +^direction +^content )} +``` + +The “<>” must be placed directly _before_ the value it compares, and +in the example above, it is correctly before wall. The rule can also be +written with the variable _after_ the test that the content is not equal +to wall: + +```Soar +sp {propose*move*2a +(state ^io.input-link.my-location..content +{ <> wall }) +--> +( ^operator + =) +( ^name move +^direction +^content )} +``` + +The not equal test can also be used with variables. For example, if you +want to test that the content of the cells to the north and south of the +eater are not equal, you could use the following conditions: + +```Soar +(state ^io.input-link.my-location ) +( ^north.content +^south.content <> ) +``` + +If you wanted to match the contents of both the north and south cells +for use in the action, you could use the following conditions: + +```Soar +(state ^io.input-link.my-location ) +( ^north.content +^south.content { <> }) +``` + +Remember, the not equal test, “<>”, must directly precede the symbol +or variable it refers to. Soar also has tests for greater than (>), +less than (<), greater than or equal to (>=), and less than or equal +to (<=). These can be used when matching numbers and also precede the +value they refer to. For example, to test that the eater’s score is +greater than 25, the following condition can be used. +`(state ^io.input-link.eater.score > 25)` + +### Move Operator Application + +This application rule for the move operator is a copy of the +move-to-food operator application. The English and Soar versions are the +same except for the name of the operator: + +```Soar +# Apply*move +# If the move operator for a direction is selected, +# generate an output command to move in that direction. +``` + +```Soar +sp {apply*move +(state ^io.output-link +^operator ) +( ^name move +^direction ) +--> +( ^move.direction )} +``` + +```Soar +# Apply*move*remove-move: +# If the move operator is selected, +# and there is a completed move command on the output link, +# then remove that command. +``` + +. + +```Soar +sp {apply*move*remove-move +(state ^io.output-link +^operator.name move) +( ^move ) +( ^status complete) +--> +( ^move -)} +``` + +The proposal rule together with these two rules will give you an eater +that randomly moves around, avoiding walls. We can greatly improve its +behavior by using more preferences. + +### Move Operator Selection + +To improve the performance of the eater, we can add rules that prefer +moving to bonusfood over normalfood or an empty cell and prefer moving +to normalfood over moving into an empty cell or a cell with another +eater. The preference language in Soar is rich enough to support a +variety of ways of ordering the choices and we will explore different +possibilities in this section. + +To get started, you need to create a rule to prefer bonusfood to +normalfood or empty or an eater. The condition part of the rule must +match against operator proposals, while the action part must prefer the +operator that moves to the bonusfood. In English this would be: + +````Soar +# Select*move*bonusfood-better-than-normalfood +# If there is a proposed operator to move to a cell with bonusfood and +# there is a second proposed operator to move to a cell that is empty or +# has normalfood or another eater +# prefer the first operator. +`` + +The conditions of this operator must match against proposed operators +before they have been selected. A proposed operator can be matched by +matching the acceptable preference for the operator, which is written in +the condition as the ^operator augmentation of the state, with a value +for the operator identifier, followed by a plus sign, “+”. +(state ^operator +) + +Acceptable preferences are the only preferences that are added to +working memory. All of the other preferences (better, best, worse, +worst, and reject) are not added to working memory. They are held in +preference memory and persist as long as the rule instantiations that +created them still match. They are not in working memory because there +is little advantage to having them available for other rules to match +against, whereas the acceptable preferences must be in working memory +because they define what operators are candidates for selection. + +An operator can be preferred by creating a *better* than preference and +the decision procedure will use that preference in determining which +operator to select – it will never select an operator if it is worse +than another candidate operator, unless that candidate is rejected. In +addition to the better preference, there is a *worse* preference that is +exactly the opposite. In an action, the better preference is the greater +than sign: “>”. It is used in the same place as an acceptable or +indifferent preference, except that a variable that matched the +identifier of the better operator is put before the greater than sign, +and the variable that matched the identifier of the worse operator is +put after the greater than sign. Therefore, the Soar rule is as follows: + +```Soar +sp {select*move*bonusfood-better-than-normalfood-empty-eater +(state ^operator + +^operator +) +( ^name move +^content bonusfood) +( ^name move +^content << normalfood empty eater >>) +--> +( ^operator > )} +```` + +This rule will match two different proposed operators (operators with +acceptable preferences), `` and ``. Both have name move, while +one matches operators that move to bonusfood while the other will match +operators that move to normalfood, empty squares, or squares with +another eater. + +If there are adjacent cells with both bonusfood and normalfood, this +rule will fire right after propose\*move creates acceptable preferences, +but during the same proposal phase so that it will influence the next +operator selection. It will fire multiple times if there are multiple +cells with bonusfood or normalfood. After these preferences are created, +the decision procedure will gather them up to make a decision. + +You can use exactly the same approach to prefer moving to cells with +normalfood over moving to empty cells or cells with eaters. Soar +provides an alternative with the _worst_ preference, which means don’t +select the operator unless there are no other choices. In this case, you +can create worse preferences for operators that move the eater into an +empty cell or a cell with another eater. With these additional +preferences, operators that move into cells with bonusfood or normalfood +will always be selected if they exist, and otherwise the eater will +randomly select between moving into an empty cell or into a cell with +another eater. The new selection rule can be written in English as: + +```Soar +# Select*move*avoid-empty-eater +# If there is a proposed operator to move to an empty cell or a cell with +# another eater, +# then avoid that operator. +``` + +Here we use the word avoid to mean that the operator will only be +selected if there is nothing worse. A worst preference is written as a +less than sign: “<”. This is similar to a worse preference, except that +there is no second variable that the first variable is compared to. + +```Soar +sp {select*move*avoid-empty-eater +(state ^operator +) +( ^name move +^content << empty eater >>) +--> +( ^operator <)} +``` + +Just as there is a worst preference, there is also a _best_ preference. +The best preference means that an operator should be selected as long as +there is no other operator better than it (or it is not worse than +another operator is). Thus the meaning of best is a bit odd in that +better preferences are more important than the best preferences and an +operator with a best preference will not be selected if another operator +is better than it. + +You could have used a best preference for a move into a cell with +normalfood instead of using the worst preference. The move to normalfood +would then be selected over a move to an empty cell or a cell with an +eater. Select*move*bonusfood-better-than-normalfood-empty-eater will +ensure that bonusfood is preferred to normalfood. + +```Soar +# Select*move*prefer-normalfood +# If there is a proposed operator to move to a cell with normalfood, +# prefer that operator. +``` + +```Soar +sp {select*move*prefer-normalfood +(state ^operator +) +( ^name move +^content normalfood) +--> +( ^operator >)} +``` + +### Summary of Preferences + +This section summarizes the meaning of the preferences presented +earlier. There are two additional preferences (require and prohibit) +that are only rarely used and are not covered in this tutorial, but are +described in the Soar 8 Manual. The preferences can be thought of as a +sequence of filters, processed in the following order. + +- **Acceptable (+)** An acceptable preference states that a value is a + candidatefor selection. Only values with an acceptable preference + have the potential of being selected. If there is only one value + with an acceptable preference, that value will be selected if it + does not also have a reject preference. + +- **Reject (-)** A reject preference states that the value is not a + candidatefor selection. A value will not be selected if it has a + reject preference. + +- **Better (>), Worse (<)** These preferences state that a value + should not be selected if the better value is a candidate. If the + better value does not have an acceptable preference, or is rejected, + the better/worse preference is ignored. Otherwise, the worse value + is removed from consideration. Better and worse are simple inverses + of each other, so that A better than B is equivalent to B worse than + A. + +- **Best (>)** A best preference states that the value should be + selected if it is not rejected, or if there is no other value better + than it. If a value is best (and not rejected, or worse than + another),it will be selected over any other value that is not also + best. If two values are best, then the remaining preferences (worst, + indifferent) will be examined to determine the selection. If a value + (that is not rejected) is better than a best value, the better value + will be selected. (This result is counter-intuitive, but allows + explicit knowledge about the relative worth of two values to + dominate knowledge of only a single value.) + +- **Worst (<)** A worst preference states that the value should be + selected only if there are no alternatives. A worst will only be + considered if the above preferences have not filtered the choices to + a single choice. In this case, any value with a worst preference + will be discarded, unless all of the values have worst preferences. + +- **Indifferent (=)** An indifferent preference states that there is + positive knowledge that it does not matter which value is selected. + This may be a binary preference, to say that two values are mutually + indifferent, or a unary preference, to say that a single value is as + good or as bad a choice as other expected alternatives. Indifferent + preferences are used to signal that it does not matter which + operator is selected, which results in a random selection is made + from among the alternatives. + +### Example Run + +The following trace shows how Soar fires and retracts many rules in +parallel, with operator proposals following selections. + +![](Images/02/image27.png) + +## Advanced Move Operator\*\* + +The eater you created in the last section will greedily consume food and +never get stuck. However, its behavior doesn’t always appear to be very +intelligent, especially if it is surrounded by empty cells. The specific +problem addressed in this section is that the eater will sometimes move +randomly back and forth when it is surrounded by empty cells. That is +clearly wasted effort and the eater should at least move to a cell +different from the one it just came from. There are other approaches to +improving the eater, such as having it test for food two spaces away, +but the point of this section is to avoid moving back to the same cell, +which will give you experience with creating persistent structures in +working memory. + +In order for the eater to avoid moving back to the cell it just came +from, the eater must remember which direction it moved to get to the +current cell. That information is available as an augmentation of the +move operator while the eater is moving, but it disappears, along with +the operator, once the move is completed. What is needed is a persistent +augmentation of the state that records the direction of the last +operator. This direction can then be used to avoid moving in the +opposite direction. You will need to change the operator proposal to +avoid moving back to the prior cell and the operator application to +record the direction of the prior cell. A further change is needed to +initialize some data structures in working memory that will make it easy +to determine the opposite of each direction, which is needed to avoid +moving back to the last cell. + +In this section we start with the initialization rules, and then cover +the operator application rules and operator proposals. + +### Initialization: Opposite Directions + +The rules that you will write for operator application and selection +need to determine the opposite of the direction the eater moved last, +because that is the direction that needs to be avoided. The easiest way +to do this is to create a structure in working memory that contains each +direction, and each direction has an attribute that is its opposite +direction. This structure can be on the state and the following rule +creates one possible structure that encodes this information: + +```Soar +sp {initialize*state*directions +(state ^type state) +--> +( ^directions ) +( ^value north ^opposite south) +( ^value east ^opposite west) +( ^value south ^opposite north) +( ^value west ^opposite east)} +``` + +This is the first rule you’ve seen where there is an action with +multiple values `( )` for a single attribute +(^direction). Each value can be written as individual actions; however, +as a shortcut, all of the values can be listed following their common +attribute. + +One might be tempted to have the values of ^direction be the symbols +north, east, south, and west. However, these values cannot be further +augmented with their opposites; only identifiers can have augmentations. +It is not legal to have augmentations of the form: (north ^opposite +south). + +Rules such as initialize*state*directions are quite common in Soar +program because they create fixed working memory structures. These +structures simplify operators by making it possible to directly match +relations such as opposite instead of having to write rules that are +specific to each of the directions. This rule will fire during the first +cycle when (s1 ^type state) is added to working memory. The structures +it creates will have i-support because the rule conditions do not test +an operator. However, the structures will never be removed because the +conditions match throughout the life of the eater. + +### Advanced Move Operator Application + +Our planned extension to the move operator does not require any changes +to the existing application rules. However, you must add two rules to +maintain a memory of the last direction moved. One of the rules must +create the memory by adding the persistent (o-supported) structure when +an operator is applied, and one must remove the old value when the next +operator is selected. (You may think that you could write a single rule +that both creates the last-direction and removes any old value for +last-direction, but then a second rule would have to be written to cover +the initial situation when there is no existing last-direction.) Try to +write the English and Soar versions of these two rules. + +```Soar +# Apply*move*create\*last-direction +# If the move operator for a direction is selected, +# create an augmentation called last-direction with that direction. + +sp {apply*move*create\*last-direction +(state ^operator ) +( ^name move +^direction ) +--> +( ^last-direction )} +``` + +```Soar +# Apply*move*remove*last-direction +# If the move operator for a direction is selected, +# and the last-direction is not equal to that direction, +# then remove the last-direction. + +sp {apply*move*remove\*last-direction +(state ^operator +^last-direction ) +( ^direction <> +^name move) +--> +( ^last-direction -)} +``` + +Since both of these rules test the current operator, their actions are +persistent. The first rule creates an augmentation of the state with +attribute ^last-direction. This augmentation will always contain the +direction of the last operator applied by copying the direction from the +move operator. The next rule removes the ^last-direction attribute if it +does not equal the direction on the current operator by using a reject +preference. These rules will fire during the operator application phase. + +Why can’t you use an i-supported rule? Conceptually, you need the +^last-direction augmentation to persist after the operator is no longer +selected because it is going to be tested to select and apply the next +operator. More pragmatically, Soar will automatically make the +augmentation o-supported because it is created as part of an operator +application. + +This section and the previous one demonstrate two different ways to +create persistent structures in working memory. If the structure can +persist throughout the life of the eater, you can use a rule that tests +that the state exists and then creates the structure in working memory +in the action. Whenever the structure must change during the life of the +eater, you must use a pair of rules that are part of the application of +an operator - one to add the structure when appropriate, and one to +remove the structure when it is no longer appropriate. + +### Advanced Move Operator Proposal + +There are two ways to modify the proposal and selection rules so that +the eater does not move backward. One requires modifying the proposal +rule so that the operator is never proposed, and the second is to create +a new selection rule that creates a preference that prevents the +operator from being selected. We will look at both possibilities. + +In order to modify the proposal rule, recall the final move proposal: + +```Soar +# Propose*move: +# If the content of an adjacent cell is not a wall, +# propose move in the direction of that cell, with the cell’s content, +# and indicate that this operator can be selected randomly. +``` + +A condition can easily be added that tests that the direction of the +adjacent cell () is not equal to the opposite of the direction of +the last move. However, this will not work for the first move when there +is no direction for the last move because no operator has applied to +create the memory of a previous move. An alternative is test that there +does not exist an opposite of last direction that is equal to the +direction of the adjacent cell. How is that different? The first tests +that there does exist in working memory an augmentation of the state, +but its value is not equal. The second tests that there does not exist +in working memory an augmentation that is equal. The second one will be +correct for the first move. Thus the revised English description is: + +```Soar +# Propose*move*no-backward: +# If there is normalfood, bonusfood, eater, or empty in an adjacent cell, +# and there is no last direction equal to the opposite direction for that cell, +# propose move in the direction of that cell, with the cell’s content, +# and indicate that this operator can be selected randomly. +``` + +To test for the absence of an augmentation, Soar uses a dash, “-“. The +dash, called negation, precedes the augmentation that must not be in +working memory for the rule to match. The new proposal rule is as +follows: + +```Soar +sp {propose*move*no-backward +(state ^io.input-link.my-location..content { <> +wall } +^directions + +-^last-direction ) +( ^value +^opposite ) +--> +( ^operator + =) +( ^name move +^direction +^content )} +``` + +A negation can also precede a complete object, where the identifier is +followed by multiple attribute-values. In that case, its is a test that +there is no object with all of those attributes in working memory. The +semantics of using negation can be a bit tricky and the details of it +are spelled out in the manual. + +### Advanced Move Operator Selection + +The alternative to modifying the proposal rule is to add a rule that +eliminates from consideration any operator that moves opposite to the +last direction. This requires a new preference, called reject, +represented by a dash, “-“. This is different from the operator +application action that is also called rejection because that rejection +removes a working memory element. When a reject preference is created +for an operator, the decision procedure will not select that operator, +no matter which other preferences have been created. However, the reject +preference will be retracted when the rule that creates it no longer +fires, allowing the rejected operator to be selected when the situation +changes. Reject is used when an operator should not be selected for the +current situation even if there are no other options. In contrast, a +worst preference is used when an operator can be selected, but only if +there are no better options. + +One side effect of reject is that it disables better/worse preferences +where the rejected operator is better than some other operator. +Normally, that other operator would not be selected, but if the better +operator is rejected, the better/worse preference is ignored. + +Try writing both English and Soar versions of this rule. + +```Soar +# Select*move*reject*backward +# If there is a proposed operator to move in the direction +# opposite the last move, +# reject that operator. + +sp {select*move*reject*backward +(state ^operator + +^directions +^last-direction ) +( ^value +^opposite ) +( ^name move +^direction ) +--> +( ^operator -)} +``` + +Note that this rule will not reject the proposed operator for the first +move because the attribute ^last-direction will not initially exist. + +## Jump Operator + +All of the eaters you’ve created so far have a single type of operator: +move. In many situations, more than one instance of the operator is +created, but still, there was only one operator. Adding operators does +not change the way Soar operates, but it does give you a chance to use +what you’ve learned on a slightly different problem. In this section, +you are going to write the jump operator. An eater can jump over a cell +to a cell two moves away; however, a jump costs the eater 5 points (the +same as it gains from a normalfood). The eater can jump over a wall, but +it cannot jump into a cell with a wall. + +In writing the jump operator, you should write and test the proposal and +application rules without including the move operator. This will let you +debug the jump rules first, without getting things mixed up with the +rules for the move operator. You should then write operator selection +rules and combine your jump operator with the move operator. In writing +the jump operator, you will want to use the initialization rule for +directions from the previous section, so you will want to copy that to +your file in which you are writing the jump operator. + +The rest of this section goes through the rules for jump. If you feel +confident in your knowledge of the aspects of Soar that have been +presented so far, you should try to write your own jump operator before +reading this section. As you are writing your own jump operator, you +should try to reuse or generalize some of the rules you wrote for the +move operator. You might find it is a bit tricky for your eater to +correctly keep track of the previous direction it moved or jumped. If +you get stuck, or are unsure about what to do, you should read these +sections. + +After you finish this section, you can try your own strategies by +creating different eaters with different control knowledge. You can even +have contests between them to see which strategies work best. + +### Jump Operator Proposal + +There are only two differences between the proposal for the move +operator and the jump operator. + +- The first difference is that the name of the operator should be + jump, not move. + +- The second is that instead of testing that an adjacent cell does not + contain a wall, the jump operator needs to test that a cell two + moves away in a direction does not contain a wall. This is easy to + add because every cell has the same four directional pointers, so + the desired cell can be tested via the direction augmentation on the + adjacent cell: instead of just use . which + tests two steps in the same direction because the same directional + pointer must match both uses of . + +In these examples, we will use the proposal that does not test for the +absence of backward moves. We will include that in the selection +knowledge. Thus the English version is: + +```Soar +# Propose*jump: +# If the content of a cell two steps away in a direction is not a wall, +# propose jump in the direction of that cell, with the cell’s content, +# and indicate that this operator can be selected randomly. + +sp {propose*jump +(state ^io.input-link.my-location...content +{ <> wall }) +--> +( ^operator + =) +( ^name jump +^direction +^content )} +``` + +### Jump Operator Application + +The application of jump is exactly the same as the application of move, +except that the jump name must be issued instead of a move. Instead of +creating a new rule for each operator, you can reuse and generalize the +original operator application rules by allowing them to match an +operator named either move or jump, and then copying the operator name +to the output link. + +```Soar +# Apply*move*jump +# If the move or jump operator for a direction is selected, +# generate an output name to move in that direction. + +sp {apply*move +(state ^io.output-link +^operator ) +( ^name { << move jump >> } +^direction ) +--> +( ^.direction )} +``` + +```Soar +# Apply*move*jump*remove-name: +# If the move or jump operator is selected, +# and there is a completed action () on the output link, +# then remove that name. + +sp {apply*move*remove-move +(state ^io.output-link +^operator.name ) +( ^ ) +( ^status complete) +--> +( ^ -)} +``` + +### Simplified Operators + +In writing the operator application rules for the jump operator, you +have probably noticed that both operators include a rule to create the +action command (move or jump) on the output-link, and then remove that +command when it has completed. You can simplify the writing of future +operators by writing two general rules that perform these functions for +every operator that performs external actions. + +The first rule creates the action command on the output-link. To make +this a completely general rule, you need to have a standard way of +representing action commands on operators so that a single rule can +create the commands on the output-link. The simplest convention is to +have the action command be an augmentation of the operator, such as: +`^jump.direction `. Another possibility would be to have a +specific augmentation on the operator, say ^actions, that had the +commands as subobjects: + +```Soar +( ^actions.jump.direction ) +``` + +You probably want to adopt this second approach because it makes it +possible to write a single rule that copies the action commands to the +output-link without risk of copying other augmentations. Under that +convention, the general command creation rule is: + +```Soar +sp {apply\*operator\*create-action-command +(state ^operator.actions. +^io.output-link ) +--> +( ^ )} +``` + +The variable will match jump and the value will match the +identifier of the object that has direction as an augmentation. It +copies ^jump and that identifier on to the output-link. + +The second general rule removes a completed action command. + +```Soar +sp {apply\*operator\*remove-command +(state ^operator.actions +^io.output-link ) +( ^ ) +( ^status complete) +--> +( ^ -)} +``` + +With these rules included in an Eater, both the move and the jump +operator require only proposals (except when other actions are +performed, such as remembering prior moves). To use both of these rules, +you need to modify the proposal rules so that they create the actions +structure. Below is an example of the jump operator proposal. + +```Soar +sp {propose\*jump +(state ^io.input-link.my-location...content +{ <> wall }) +--> +( ^operator + =) +( ^name jump +^actions.jump.direction +^content )} +``` + +Rules like this simplify the writing of Soar programs by creating +conventions; however these conventions also restrict what can be written +in a Soar program. For example, if this rule is used in a Soar program, +then the status complete augmentation will always be removed and will +not be available for other rules to test. For this domain, it doesn’t +seem like that will be a problem, and it might not be for others as +well. It is just that before creating a rule that applies to all +operators, you should be careful. Throughout this tutorial you will see +additional rules that are used to simplify the writing of programs. We +have tried to be careful in selecting these types of rules. + +### Jump Operator Selection + +There are many strategies for selecting jump operators. One simple +strategy is to prefer operators that jump into cells with bonusfood to +operators that move into empty cells, while rejecting operators that +jump into empty cells. We can generalize some of the rules for move to +cover both jump and move. + +```Soar +sp {select*move*bonusfood-better-than-normalfood-empty-eater +(state ^operator + +^operator +) +( ^name { << jump move >> } +^content bonusfood) +( ^name +^content << normalfood empty eater >>) +--> +( ^operator > )} +``` + +```Soar +sp {select\*jump\*bonusfood-better-than*move*empty +(state ^operator + +^operator +) +( ^name jump +^content bonusfood) +( ^name move +^content empty) +--> +( ^operator > )} +``` + +### Jump and Move Operator Selection + +In order to cover all of the cases of move and jump operators for all of +the different cell contents, you would need to write lots of rules. Is +there an easier way? One way is to translate the different names and +contents into numbers that correspond to the number of points the eater +will get. A rule could then compare the numbers and create better +preferences for operators with higher numbers. You can translate the +names and contents into numbers by writing one rule for each name and +content pair; however, it is much easier to create a structure in +working memory with the information and have a general rule match +against it and do the translation. This is similar to the way opposite +directions were computed. + +The first rule to write is the one that creates the structure in working +memory. It needs to include an object for each pair of name and content, +and for each pair, the expected value to the eater. + +```Soar +sp {init*elaborate*name-content-value +(state ^type state) +--> +( ^name-content-value + + ) +( ^name move ^content empty ^value 0) +( ^name move ^content eater ^value 0) +( ^name move ^content normalfood ^value 5) +( ^name move ^content bonusfood ^value 10) +( ^name jump ^content empty ^value -5) +( ^name jump ^content eater ^value -5) +( ^name jump ^content normalfood ^value 0) +( ^name jump ^content bonusfood ^value 5)} +``` + +To compare two operators, you can write a rule that matches the +operators and the name-content-value structure, but it is easier to +understand the reasoning if you break it into two rules. The first rule +matches each operator and the appropriate name-content-value and copies +the value onto the operator. + +```Soar +sp {elaborate\*operator\*value +(state ^operator + +^name-content-value ) +( ^name ^content ) +( ^name ^content ^value ) +--> +( ^value )} +``` + +Now you can write a rule that compares the values associated with each +operator and then creates a better preference for the operators with +higher values. + +```Soar +sp {select\*compare\*best\*value +(state ^operator + +^operator +) +( ^value ) +( ^value < ) +--> +( ^operator > )} +``` + +During a run, the first rule will fire for every proposed operator, and +the second rule will fire for every pair of operators with different +values. All of these firings, including the proposals of the operators, +happen during the proposal phase. + +## Advanced Eaters + +If you want to build more complex eaters, here are a few ideas. You will +find it easier to build some of these after you complete the next part +of the tutorial. + +1. An eater that chases another eater. +2. An eater that tries to stay away from other eaters. +3. An eater that systematically searches for food when empty cells + surround it. +4. An eater that looks ahead to see what move would be better based on + what surrounds the cell it is going to move into. +5. An eater that takes advantage of the fact that bonus food comes in + vertical lines, each 4 cells apart. + +## Top-state Structure + +This page has a summary of the structure of the top-state for every +eater. It is a useful reference when you build your own eaters and you +will probably want to make a copy of it. All of the attributes of an +object appear below it in outline form, with indentation used to signify +sub-objects. Possible values are listed on the same line as the +attribute. + +``` +^io +^input-link +^eater +^direction east/north/south/west +^name red/blue/yellow/green/purple/black +^score 0-1000 +^x 1-15 +^y 1-15 +^my-location +^content bonusfood/normalfood/eater/empty/wall +^east +^content bonusfood/normalfood/eater/empty/wall + +... +^north +^content bonusfood/normalfood/eater/empty/wall + +... +^south +^content bonusfood/normalfood/eater/empty/wall + +... +^west +^content bonusfood/normalfood/eater/empty/wall + +... +^output-link +^move +^direction east/north/south/west +^status complete - created by Soar as feedback +^jump +^direction east/north/south/west +^status complete - created by Soar as feedback +^superstate nil +^type state +``` diff --git a/docs/tutorials/soar_tutorial/03.md b/docs/tutorials/soar_tutorial/03.md new file mode 100644 index 00000000..c6f43a7f --- /dev/null +++ b/docs/tutorials/soar_tutorial/03.md @@ -0,0 +1,2554 @@ +# Part III: Subgoals + +This part of the tutorial will teach you to build Soar programs that use +subgoals to dynamically decompose complex problems into simpler +problems. This part starts with a description of TankSoar. This is +followed by an exercise where you will build a simple tank that wanders +about. From there, subgoals and high-level operators are developed. The +intermediate sections teach you to write high-level operators. The end +concentrates on refinements of these operators so that your tank is more +effective. + +You should use VisualSoar in developing your tank. The release includes +default datamaps for the input and output links so that you do not have +to define them yourself. You use them by opening the default project and +then using the SAVE AS feature in Visual Soar to save the project under +a new name. You will need to modify the datamap and included rules to +use the new name of your tank (if you desire). + +## TankSoar + +TankSoar is similar to Eaters in that your Soar program will control a +tank in a grid-based world with walls. However, in TankSoar, your tank +has many more sensors and many more actions than the eaters did. There +are also more interactions between tanks (they shoot at each other), and +all of these factors mean that the TankSoar programs can be much more +complicated than those you developed for Eaters. + +![](Images/03/image1.jpeg) + +Below is an example of the main map in TankSoar. + +### Installing and Playing TankSoar + +To launch TankSoar, open the directory where you installed it and launch +the appropriate script file (TankSoar.bat on Windows, TankSoar.sh on Mac +or Linux) + +Within the top level folder, there will be an +“Agents\\TankSoar\\Tutorial” directory, which is where you will +create directories and files for Soar tanks. (If you installed the +stand-alone version of TankSoar rather than the full tutorial, this +directory will simply be “Agents\\Tutorial”). + +After starting TankSoar, you will have a new window on your computer +screen: + +![](Images/03/image2.png) + +This is the TankSoar environment for creating tanks and controlling the +game. + +Tanks are created, modified and destroyed much as the Eaters were in +Part 2 of this Tutorial: Press the “New” button in the “Agents” area of +the TankSoar game and follow the dialogs to select productions that will +be loaded when a Tank is created. + +## Tank Resources + +Each tank has three resources. A summary of these is shown, along with a +score, for each tank to the right of the Map window. + +- _Health_ + A tank has a maximum of 1000 health points, and dies when its health + goes to 0. When a tank dies, it is resurrected at a random open + square with the initial values of all of its resources (health=1000, + energy=1000, missiles=15). When created, a tank has 1000 health + points. If a missile hits a tank while its shields are down, its + health decreases by 400. A tank's health is increased when it sits + on a healthcharger at a rate of 150 per turn. + +- _Energy_ + A tank has a maximum of 1000 energy points. A tank's energy is + decreased when it uses its radar (proportional to the range it has + set the radar) or when it uses its shields (20 units per turn). A + tank's energy is increased when it sits on an energycharger at a + rate of 250 per turn. A tank's energy is decreased by 250 if it is + hit by a missile while its shields are up. If a tank’s energy goes + to 0, it will not be able to use its radar or shields until it + recharges (or dies). + +- _Missiles_ + A tank starts off with 15 missiles. A tank's supply of missiles is + increased by 7 when it picks up a pack of missiles. A tank's supply + of missiles is decreased by one each time it fires a missile. + +![](Images/03/image3.jpeg) + +### Tank Primary Sensors + +A tank has six primary sensors for perceiving its world. All of these +sensors are always active, except the radar sensor, which must be turned +on to use. The information from these sensors is made available to a +tank on the input-link. The structure of the input-link augmentation is +shown for each sensor. + +- _Blocked sensor_ + The blocked sensor detects whether the squares immediately adjacent + to a tank are blocked or open (yes=blocked, no=open). A square can + be blocked by an obstacle or by another tank, but the blocked sensor + gives no information to distinguish between these cases. The blocked + sensor will be updated on the input link (“blink”) even if the tank + doesn’t move or if the sensor does not otherwise change. + +^blocked +^backward yes/no +^forward yes/no +^left yes/no +^right yes/no + +- _Incoming sensor_ + The incoming sensor detects whether there is a missile approaching a + tank at any distance, unless the missile is on the other side of an + obstacle or tank. It does not detect a tank's own missiles. + +^incoming +^backward yes/no +^forward yes/no +^left yes/no +^right yes/no + +- _Radar sensor_ + The radar is functional when a tank has turned the radar on and has + enough energy to power it. This sensor detects objects in front of + the tank in a column that is three squares wide. The distance _d_ + which the radar can see is either the current radar setting or the + number of squares between the tank and the closest blocking object + (i.e. tank or obstacle) in front of it, whichever is lower. The + objects visible on the radar are those that are a distance of _d_ + squares or less away from the tank directly in front of it, directly + in front of one square to the left of it, and directly in front of + one square to the right of it. Radar also picks up any objects + directly to the right or left of the tank. Additionally, if the + effective distance is greater than _d_, i.e. the radar is being + blocked by an object, then the object(s) _d+1_ squares directly in + front of this tank is also visible on the radar. Multiple objects in + the same square are visible if their square is visible. The + information returned about visible objects is their type and where + they were seen (e.g. an obstacle 5 squares in front on the left). If + an object is a tank, then its color is also given. + ^radar + ^energy + ^distance 0-d + ^position left/center/right + ^health + ^distance 0-d + ^position left/center/right + ^missiles + ^distance 0-d + ^position left/center/right + ^obstacle + ^distance 0-d + ^position left/center/right + ^open + ^distance 0-d + ^position left/center/right + ^tank + ^distance 0-d + ^position left/center/right + ^color red/blue/purple/… + +- _Rwaves sensor_ + The rwave sensor detects if the radar of another tank is detecting + the tank from the four directions. + ^rwaves + ^backward yes/no + ^forward yes/no + ^left yes/no + ^right yes/no + +- _Smell sensor_ + The smell sensors detects the closest tank, and provides information + on how close that tank is and what its color is. If there are two or + more tanks equally close, then one of them is chosen at random. The + distance is the number of cells in x and y between the two tanks + (Manhattan distance). Smell penetrates obstacles, so the smelled + tank may not be the tank that is closest to move to. If there are no + other tanks, the value of both color and distance will be _none_. + ^smell + ^color none/red/blue/purple/… + ^distance none/0-28 + +- _Sound sensor_ + The sound sensor detects the closest tank that moved during the last + decision, as long as that tank is currently 7 or less squares away. + If two or more tanks moved during the last decision and are equally + close, then the sensor chooses one randomly. If a tank is within 7 + squares but did not move during the last decision, it will not make + a noise and will not be picked up by this sensor. The information + returned about the closest tank is the direction to move on the + shortest path toward the sensed tank. If there is more than one + possible direction, then the sensor chooses one randomly. If there + is no tank within 7 squares that moved, the sound sensor will have + value _silent_. + ^sound silent/left/right/forward/backward + +You can select a tank by clicking on the tank in the Main Map Window. +The sensors of the selected tank are shown to the right of the Map. + +![](Images/03/image4.png) + +### Tank Secondary Sensors + +In addition to the primary sensors, a tank has additional sensors that +give it information about its own state. These sensors are available via +the input-link and are always functional. + +- _Clock_ + +Clock is initialized to 1 and is incremented each decision. It is a +global counter for all tanks and thus, if a tank is created after others +have run for a while, the initial value will not be 1. +^clock 1-N + +- _Direction_ + +The direction that the tank is facing: north, east, south, or west. It +changes when the tank turns. +^direction north/east/south/west + +- _Energy_ + +The current energy level. If energy is 0, the tank will not be able to +use its radar or shields. +^energy 0-1000 + +- _Energy recharger_ + +If a tank is on an energy recharger (battery), this has value yes. +Otherwise it is no. +^energyrecharger no/yes + +- _Health_ + +The current health level. If health is 0, the tank dies and is +resurrected at a random square. +^health 0-1000 + +- _Health recharger_ + +If a tank is on a health recharger, this has value yes. Otherwise it is +no. +^healthrecharger no/yes + +- _Missiles_ + +The number of missiles the tank currently has. +^missiles 0-N + +- _My-color_ + +The color of the tank. +^my-color blue/red/purple/… + +- _Radar-distance_ + +The effective distance of the radar the last time it was used. This is +the distance that the radar reached before it was blocked. If there were +no obstacles, it will be the same as the radar-setting. +^radar-distance 1-14 + +- _Radar-setting_ + +Radar-setting is the distance that the radar has been set to using the +radar-power output command. +^radar-setting 1-14 + +- _Radar-status_ + +Radar-status tells whether the radar is turned on or not. +^radar-status on/off + +- _Random_ + +A random real number between 0 and 1. It changes every decision. +^random 0.0-1.0 + +- _Resurrected_ + +Whenever a tank dies and is resurrected, this has value yes. +^resurrected no/yes + +- _Shield-status_ + +Shield-status tells whether the shields are turned on or not. +^shield-status on/off + +- _X_ + +X is the x location on the map. X goes from 1 (left side) to 14 (right +side). +^x 1-14 + +- _Y_ + +Y is the y location on the map. Y goes from 1 (top) to 14 (bottom). +^y 1-14 + +### Tank Actions + +A tank has several actions it can perform. All actions are performed by +augmenting the output-link. All actions can be performed in parallel +except for move and rotate. + +- _Move_ + A tank can move forward, backward, left, or right. Moving is + mutually exclusive with rotating. If a tank tries to move but is + blocked, it remains where it is and loses 100 health units. A tank + may also move in no direction, which indicates a wait action. + ^move.direction left/right/forward/backward/none + +- _Rotate_ + A tank can rotate left or right. Rotating is mutually exclusive with + moving. A rotate will never fail (assuming the tank is neither + killed nor tries to simultaneously move). + ^rotate.direction left/right + +- _Fire_ + A tank can fire one missile per decision. Firing can be done in + conjunction with any other action. The missile is fired straight + ahead in the direction the tank is facing. Firing a missile + decreases a tank's missile supply by one, and it will fail if a tank + has no missiles. This failure will be reflected by the tank's + missile supply remaining zero. + ^fire.weapon missile. + +- _Radar_ + A tank can turn its radar on and off. Turning the radar on will fail + if a tank does not have enough energy to supply the radar. This + failure will be reflected in that ^radar-distance will adjust itself + to the highest actually attainable level. + ^radar.switch on/off + +- _Radar Range_ + +A tank can change the range of its radar by using the radar-power +command. The value can be from 1 to 14. The higher the power setting of +the radar, the more energy is used. +^radar-power.setting 1-14 + +- _Shields_ + A tank can turn its shields on and off, and this can be done in + conjunction with any other action. Turning shields on uses 20 energy + units per decision. It will fail if a tank does not have enough + energy. The failure will be reflected in that ^shield-status will be + off. + ^shields.switch on/off + +### Types of Game Objects + +The following are the objects that can appear on the map: + +- ![](Images/03/image5.png)_Obstacles_ + Obstacles look like trees and they are there to get in the way. They + never move and they cannot be blown up. There will not be any part + of the playing field that is inaccessible purely due to wall + placement. (Part of the playing field could be temporarily + inaccessible due to a tank blocking the way.) + +- ![](Images/03/image6.png)_Healthcharger_ + There is one healthcharger per map. The healthcharger never moves. + For each decision a tank stays on a healthcharger, its health is + increased by 150 minus and any damage it may incur by bumping into + something. + +- ![](Images/03/image7.png)_Energycharger_ + There is one energycharger (battery) per map. The energycharger + never moves. For each decision that a tank stays on an + energycharger, its energy is increased by 250 units minus whatever + energy it uses for its shields and/or radar. + +- ![](Images/03/image8.png)_Packs of + missiles_ + Packs of missiles are scattered around in random squares. They never + move of their own accord nor can they be moved. Missiles flying + through squares with packs of missiles do not hit those packs. Packs + of missiles are created at random locations and times. A tank can + pick up a pack of missiles by moving to its square. The pack of + missiles disappears and the tank's missile supply is increased by 7. + +- ![](Images/03/image9.png)_Tanks_ + Tanks are controlled by TankSoar agents. They do fun stuff like fire + missiles and move around, as described above. + +All objects take up only one square. Obstacles cannot exist in the same +square as any other object. Both chargers will never be in the same +square. A tank cannot be in the same square as a pack of missiles +because it will pick up that pack. All other combinations of objects are +allowed together in a square. + +Tanks will never spawn on top of energychargers, healthchargers, or +missile packs. + +## Summary of Input-link and Output-link + +^io +^input-link +^blocked +^backward yes/no +^forward yes/no +^left yes/no +^right yes/no +^incoming +^backward yes/no +^forward yes/no +^left yes/no +^right yes/no +^radar +^energy +^distance 0-13 +^position left/center/right +^health +^distance 0-13 +^position left/center/right +^missiles +^distance 0-13 +^position left/center/right +^obstacle +^distance 0-13 +^position left/center/right +^open +^distance 0-13 +^position left/center/right +^tank +^distance 0-13 +^position left/center/right +^io +^input-link +^rwaves +^backward yes/no +^forward yes/no +^left yes/no +^right yes/no +^smell +^color none/red/blue/purple/… +^distance none/0-28 +^sound silent/left/right/ + +forward/backward +^clock 1-N +^direction north/east/south/west +^energy 0-1000 +^energyrecharger no/yes +^health 0-1000 +^healthrecharger no/yes +^missiles 0-N +^my-color blue/red/purple/… +^radar-distance 1-14 +^radar-setting 1-14 +^radar-status on/off +^random 0.0-1.0 +^resurrect no/yes +^shield-status on/off +^x 1-14 +^y 1-14 +^io +^output-link +^move.direction left/right/forward/backward/none +^rotate.direction left/right +^fire.weapon missile +^radar.switch on/off +^radar-power.setting 1-14 +^shields.switch on/off + +### Tank Combat + +A tank destroys another tank by hitting it with missiles. A missile can +damage any tank it hits, even the tank that fired it (in the rare case a +tank is killed and recreated in the path of its own missile). Missiles +fly at about 1.3 times as fast as tanks move. A tank that is hit by a +missile while its shields are down has its health decreased by 400 +units. If a tank is hit while its shields are up, the tank will not lose +any health but will lose 250 energy units. If a tank is sitting on a +healthcharger or an energycharger when a missile hits it, the tank dies +instantly, no matter what its health level or shield status. This is to +discourage camping out on the chargers during a battle. When a tank is +killed, it is resurrected at a random, unoccupied square on the next +decision. + +The scoring of a game is as follows: a tank gets two points for each of +its missiles that hits another tank while its shields are down and three +points for every tank it kills. A tank loses one point for each missile +that hits it while its shields are down, and two points each time it is +killed. This is to encourage aggressive, but not fanatical tactics. +Tanks have an unlimited number of lives. The game ends when one of the +tanks gets 50 points. + +If 100 game cycles elapse without any tank firing a missile, then all +tanks are reset as if they had been killed to avoid endless cycles in +agents’ behavior. No tanks incur a penalty for this respawning. + +## Tank Scheduler + +In Eaters, a new operator was selected on every decision, and every +operator performed an external action, either a move or a jump. In +TankSoar, some of the decisions will not result in new operators being +selected and some operators will perform only internal actions. If we +continue to use the scheduling approach in Eaters in TankSoar, where +each tank would get only one decision before the simulation world is +updated, tanks that are not performing external actions will appear to +just sit in place. This can be quite a disadvantage if a tank is under +attack. To avoid penalizing tanks that are “thinking” a bit, TankSoar +provides an alternative scheduler. With this scheduler, every tank is +run until it generates output commands. Usually this will be a single +decision, but sometimes will be many decisions. To handle cases where a +tank does not attempt to perform an external action given the current +situation, the tank will be run a maximum of fifteen decisions. This +parameter is set by the TankSoar environment. + +From this point on, each of your tanks will run until it has produced +new output on the output-link before another tank will run (but the +simulator will not run and change the inputs for other tanks until all +tanks have run). Below is a graphic depiction of how everything works +under this approach with three tanks. + +## Simple Wandering Tank + +In this section, you will create a tank that wanders around the board +looking for objects. This will help you to get familiar with the +TankSoar software. This tank will be the basis for a more complex tank +you will build later. + +Wandering consists of moving around the map, using sensors to avoid +bumping into obstacles and to detect other objects. A tank’s main sensor +for seeing other objects is its radar, which works from the front of the +tank. Therefore, even though a tank can move in all four directions, it +is best to move forward and turn to avoid obstacles. The radar uses up +energy, so it is best to use it sparingly. The simplest thing to do is +to turn on the radar when the tank turns, and turn it off if there is +nothing interesting to see (such as tanks, missiles, the energy +recharger, or the health recharger). One interesting issue is how much +power should be used with the radar. To simplify things, you can always +turn it on to 13, the maximum distance it can see. You will get a chance +to improve your tank later, but for now it is best if it is simple. + +This analysis leads to the three operators and the search control +described below: + +- Move: move forward if not blocked. +- Turn: if front is blocked, rotate and turn on the radar with power 13. +- Radar-off: if the radar is on and there are no objects visible, turn + off the radar. +- If radar-off is proposed, then prefer it to move. + +This set of knowledge for wandering is just the start of a more complex +tank, and you will have to add attacking other tanks, picking up +missiles, and recharging later. + +The rest of this section goes through the creation of these operator and +search control, step by step. If you feel confident that you can write +these three operators and associated search control by yourself, go +ahead and do it. All of the rules necessary to write the tanks in the +tutorial are in the directory named Agents\\TankSoar\\Tutorial. + +Throughout the development of operators for TankSoar, you will use the +general rules that were created in Eaters for copying actions from +operators to the output-link. If you use these rules, many of the +operators will have only proposals. + +To get started, create a new agent in Visual-Soar by loading in the +default agent provided in the release and then saving it as a new +project. The default agent includes the general output rules you created +for the Eaters as well as a full data map for the TankSoar input-link +and output-link. It also includes an initialization operator. This +operator adds a name to the top state as the task name, which you should +rename (probably along with the operator itself). For the rest of this +part of the tutorial, we will assume the name of the top state is +tanksoar. + +### Move Operator + +This operator is very similar to the move operator in Eaters, except +that the sensors for detecting open spaces in front of the tank are in a +different structure than the one in Eaters. + +To write the move operator, you should first write comments for operator +proposal. The application of this operator is handled by the general +rules from Eaters section 10.4. You should type these comments into your +file as documentation. + +Below is one possible version: + +```Soar +# Move Operator Proposal +# If the task is tanksoar and the tank is not blocked in the forward +# direction, propose the move operator. +``` + +For brevity, we will not include “if the task is tanksoar” in future +English descriptions, although the test for ^name tanksoar will be +included in the rules. + +In writing the Soar version of the operator proposal, you can reuse +parts of the rules you wrote in Eaters. One part of those operators that +was a bit tricky was the termination of the operator. In Eaters, we had +to make sure that the proposal would retract after the operator applied. +For Eaters that tested the cells surrounding them, no additional +conditions were required because the contents of these cells changed on +every move. The same holds true in TankSoar. Whenever the tank moves, +the working memory elements that are the attributes of the blocked +sensor change, even if the value is exactly the same. Thus, the proposal +will retract and rematch each time a tank moves. + +After writing the proposal rule, you can test it, together with the +general rules, by creating a tank with just those rules. The tank should +move forward until it is blocked. + +```Soar +sp {tanksoar*propose*move +(state ^name tanksoar +^io.input-link.blocked.forward no) +--> +( ^operator +) +( ^name move +^actions.move.direction forward)} +``` + +### Turn Operator + +The original definition of the turn operator was: + +- If the front is blocked, rotate and turn on the radar to power 13. + +Which direction should the tank turn? A reasonable approach is to turn +left or right only if that direction is not blocked. If both directions +are blocked, the tank can turn in either direction. Once the tank has +turned, it will detect that it should turn again, so that the tank would +turn completely around. Try to write the comments for the proposals. The +turn operators should be indifferent because it is possible to have both +a left and right turn proposed at the same time, and it doesn’t matter +which direction the tank turns. + +```Soar +## Turn Operator Proposal +# If the tank is blocked in the forward direction, and not blocked in the +# right or left directions, then propose the turn operator for the unblocked +# direction. Also create an indifferent preference for the operator. +# This operator will also turn on the radar and set the radar-power to 13. +# If the tank is blocked in the forward direction, and in both the right or +# left directions, then propose the turn operator left. +``` + +After the comments are written, try to write the Soar rules. Once they +are written and debugged, your tank should wander around the map +indefinitely, although it will never turn off its radar and will quickly +run out of energy. + +Below are possible rules for proposing the turns. + +```Soar +sp {propose\*turn +(state ^name tanksoar +^io.input-link.blocked ) +( ^forward yes +^ { << left right >> } no) +--> +( ^operator + =) +( ^name turn +^actions ) +( ^rotate.direction +^radar.switch on +^radar-power.setting 13)} +``` + +The propose\*turn rule uses a variable (``) to match a +blocked direction with value no, meaning that there is not an object in +the square. That attribute is restricted to match only left and right +because those are the only two directions a tank can rotate. The actions +of the rule are more complex than other rules you’ve written. The action +of this rule creates three different action commands that will be copied +in parallel to the output-link by +apply\*operator\*create-action-command. As a result, the tank rotates, +turns on its radar, and sets the radar-power all at the same time. + +Propose\*turn handles the cases where there is an open square to the +left or right. The following rule handles the case when the three +forward directions are blocked. I arbitrarily decided to have the tank +turn left to start turning around. After it rotates left once, the +propose\*turn rule will match and propose a further turn to the left. + +```Soar +sp {propose\*turn\*backward +(state ^name tanksoar +^io.input-link.blocked ) +( ^forward yes ^left yes ^right yes) +--> +( ^operator +) +( ^name turn +^actions.rotate.direction left)} +``` + +### Radar-off Operator + +In addition to moving and turning, your tank needs to turn off its radar +to conserve energy. You should write a proposal for radar-off, and some +search control so that radar-off is preferred to move. + +- Radar-off: if the radar is on and there are no interesting objects + visible, turn off the radar. The radar is left on for energy, + health, missiles, so that the tank can either pick up the object. It + is left on for tanks so they can be attacked. +- If radar-off is proposed, then prefer it to move and turn. + +The English versions of the rules for this operator are straightforward: + +```Soar +## Radar-off Operator Proposal +# If the radar is on but no energy, health, missiles and tanks visible, +# then propose the radar-off operator +## Radar-off Search Control +# If radar-off is proposed, then prefer it to move and turn. +``` + +The only tricky part of this operator is that there must be a condition +in the proposal that tests that there are not any objects on radar. +Remember that the radar’s input structures are attributes of the radar +augmentation. For every object on radar, there is an augmentation of the +radar object, where the value of the attribute is the type of object: +energy, health, missiles, obstacle, open, and tank. For example, if +there is a tank on radar, the following condition could match it: +`( ^io.input-link.radar.tank)`. You need to write a single condition that +detects if radar _does not_ detect a tank, missiles, energy, or health. +You need to include a test that the radar is on (otherwise, your rule +will turn the radar off when it is already off). + +```Soar +sp {propose\*radar-off +(state ^name tanksoar +^io.input-link ) +( ^radar-status on + +-^radar.<< energy health missiles tank >>) +--> +( ^operator +) +( ^name radar-off +^actions.radar.switch off)} +``` + +```Soar +sp {select\*radar-off*move +(state ^name tanksoar +^operator + +^operator +) +( ^name radar-off) +( ^name << turn move >>) +--> +( ^operator > )} +``` + +A different approach is to turn off the radar as part of the move +operator. Turning the radar off can happen in parallel with moving and +turning. It is implemented as an additional action of those operators. + +```Soar +sp {apply*move*radar-off +(state ^name tanksoar +^operator +^io.input-link ) +( ^radar-status on +-^radar.<< energy health missiles tank >>) +( ^name << turn move >> +^actions ) +--> +( ^radar.switch off)} +``` + +This rule is included in the all folder/directory instead of the +operator rules because it will fire in parallel with other actions. + +### Wander Trace + +If you create a tank with your wander rules and the radar-off operator +and run it, the trace will look something like this. + +![](Images/03/image10.png) + +Usually turn is followed by radar-off, but whenever there is an object +on radar, the radar will not be turned off until after the tank has +passed that object. + +## Wander Operator - Subgoals + +The rules you wrote for the previous section could be extended and rules +for chasing and attacking enemy tanks, recharging, and picking up +missiles could be added to make a more complete tank. However, you would +either have to add many more search control rules to keep the wandering +rules from firing during these other activities, or you’d have to modify +the operator proposals. Both approaches are not very appealing. It would +be better if you could package the wander rules and operators into a +unit that can be used when the tank should wander. An alternative but +related problem is that there are high-level activities that a tank can +perform involving complex combinations of low-level actions. One such +set of high-level activities consists of wandering, chasing, attacking, +and retreating. Although these are high-level activities, a tank should +be able to use its knowledge to select between these activities based on +the current situation, just as it selects between different operators. +Both of these problems are handled in Soar by allowing abstract +high-level operators to be implemented in subgoals where a new state is +created that allows low-level operators to be selected in pursuit of the +high-level operators. + +The hierarchy of operators you will build looks like the following: + +In addition to this hierarchical structure, there will be additional +rules for turning the shields on and off, and remembering sounds. These +rules are independent of the hierarchy – they will be proposed and +applied in any active goal. + +This section introduces subgoals by tracing through the proposal, +selection, and application of the high-level operator for wander. In +later sections, you will add other high-level operators for chase, +attack, and retreat. + +Visual-Soar makes this easy to do. It allows you to create the abstract +operators, such as wander and chase, and then create suboperators that +are embedded within the abstract operators. See the documentation for +Visual-Soar for details. + +The tank you will create will not be the best or smartest tank, but +building it will teach you many new things about Soar. + +### Wander Operator Proposal + +If you think of wandering as an operator, then in Soar it consists of +rules to propose it and then apply it. When should a tank wander? When +it is not attacking, chasing, or under attack by another tank. Thus, +wandering is an activity to be performed when no other tanks are +detected, or at least threatening. A tank has many senses by which it +can detect other tanks. + +- Blocked: does not tell if an obstacle is a tank, so it not useful + for deciding to wander. +- Incoming: indicates that a missile is approaching the tank. +- Radar: detects exactly where another tank is. However it is limited + to seeing in front of the tank and can be blocked by obstacles. +- Rwaves: indicates that another tank can detect the tank from a given + direction. +- Smell: detects the closest tank, but that tank may be on the other + side of a wall. +- Sound: gives the direction to another tank that is close (seven + square or less); however it does not work if the other tank has + stopped moving. + +Although rwaves is a very useful sensor in this case, in order to keep +the examples simple it is not included. You will want to add it to your +tank later on. The other sensors for detecting a nearby tank are +incoming, radar, and sound. + +```Soar +## Wander Operator Proposal +# If there is no tank detected on radar, and the sound is silent, and there +# is no incoming, then propose the wander operator. +``` + +Translating this into Soar is a bit tricky because there are two tests +for negations (no tank on radar, no incoming), but you should give it a +try. + +```Soar +sp {propose\*wander +(state ^name tanksoar +^io.input-link ) +( ^sound silent +-^radar.tank +-^incoming. yes) +--> +( ^operator +) +( ^name wander)} +``` + +### Impasses and State Creation + +All of the previous operators you’ve written have had rules to apply +them. However, wandering is not an activity that is easily represented +by individual rules – it is better represented as the repeated selection +and application of the operators you wrote in Section 2. How does Soar +make it possible to use operators, such as move and turn, to apply a +higher level operator such as wander? To find out, create a tank with +only your wander operator proposal rule. Run it for two steps. + +![](Images/03/image11.png) + +What happens is that Soar detects that there are no rules that match and +can fire when the wander operator is selected, which means that Soar +cannot make progress. In Soar, this is called an _operator no-change +impasse_, because an operator has been selected and there is no new +decision to be made. There are three other types of impasses in Soar. + +- A state no-change impasse is when no operators are proposed for the + current state. +- An operator tie impasse is when there are multiple operators + proposed, but insufficient preferences to select between them. +- An operator conflict impasse is when there are multiple operators + proposed, and the preferences conflict. + +Whenever an impasse arises, Soar automatically creates a new state (S3 +in the trace above). The purpose of this state is to provide a context +for selecting and applying operators to resolve the impasse (in the +case, moving to a state where wander is no longer selected). + +Once a substate is created, operators can be selected and applied just +as they are in the original state. These operators can change the local +state, but they can also change the contents of original state (S1) by +modifying the original state directly or by performing external actions +that indirectly lead to changes in sensors, which in turn change the +state. An impasse can also arise in the substate, which then leads to a +stack of states. In the trace above, no operator is proposed in state +S3, so another substate, S4, is created. + +To see all of the selected operators and their states, use the +print-stack command. When a substate is created, it has some initial +augmentations that distinguish it from other states. Take a look at the +augmentations for state S3 above by printing S3. + +![](Images/03/image12.png) + +The most important of these is the superstate augmentation, which has as +its value the identifier of the state in which the impasse arose (S2). +You will use this augmentation to match the wander operator and other +structures, such as the input-link and output-link. + +The processing in a substate can lead to the creation of structures that +are part of the original state (the superstate). These are called +_results_. In this example, the only results will be output commands for +controlling the tank, but results can be any structure including +operator acceptable preferences, other operator preferences, +elaborations of the state, or changes to the state that are part of an +operator application. + +All of the states in working memory are “active” in that rules can fire +for any level – it is not the case that processing in the top state +comes to a halt while the processing goes on in the substate. In fact, +it is the ability of the processing in the original state to start up +that often resolves the impasse and leads to the termination of the +subgoal. For example, in the substate for wander, we will add the +operators for moving and turning. At some point, this may cause the tank +to see and enemy tank, and fire a rule (in the top state) to propose the +attack operator; while at the same time retract the rule that proposed +the wander operator. + +Operator no-change impasses are resolved when the current operator is no +longer preferred in the original state (S1 in the trace above). That can +be because either the proposal for the current operator is retracted +(the rule creating the proposal no longer matches), or because of the +set of operator preferences changes so that another operator is +preferred. State no-change impasses are resolved when an operator has +been proposed (with an acceptable preference), so that there is at least +one candidate operator. Tie and conflict impasses are resolved when +sufficient preferences have been created so that the decision procedure +can select an operator. + +When the impasse is resolved, the substate is automatically removed from +working memory – it has served its purpose and is no longer needed. +However, all of the results that were created in the subgoal can still +persist. This is important because the results are changes to the +superstate and are usually responsible for eliminating the impasse. +Sometimes the results directly resolve the impasses, such as when a new +preference is created that makes one of the tied operators best, but +sometimes a result only indirectly resolves an impasse, such as a motor +command to a tank that causes it to turn on its radar so it sees an +enemy and decides to attack. + +Just as with all other working memory elements, the persistence of +results must be determined by Soar. This is tricky because the rule for +creating a result usually tests structures in a substate that will be +removed once the impasse is resolved. If the result is an operator +proposal, we don’t want to remove the operator when the rule that +created it no longer fires – that would defeat the purpose of the +subgoal. Instead, Soar analyzes what structures had to exist in the +superstate for that result to be produced by recursively _backtracing_ +through the conditions of the rule that created the result, finding the +rules that fired in the subgoal that created the working memory elements +that caused the rule to fire, until it finds all of the structures in +the superstate that the result depended on. It collects these together +and forms a _justification_ that is essentially a fully instantiated +rule. It then analyzes the rule to determine the persistence of the +result (i-support or o-support). If it is i-support, then the +justification is maintained until one of its conditions is no longer in +working memory and then the result is retracted. If the justification +gives o-support, then the result is maintained in working memory until +it is explicitly deleted (or removed because it becomes disconnected +from the state). + +### Wander Operator Suboperators + +As mentioned in the previous section, the wander operator is applied by +selecting and applying operators in the substate. The original rules you +wrote for wander need to be changed because they now need to apply only +if the wander operator is selected. Consider the original move operator: + +```Soar +## Move Operator Proposal +# If the state is named tanksoar and tank is not blocked in the forward +# direction, propose the move operator. + +sp {propose*move +(state ^name tanksoar +^io.input-link.blocked.forward no) +--> +( ^operator + =) +( ^name move +^actions.move.direction forward)} +``` + +This must be modified to include tests for the wander operator as the +superoperator. + +```Soar +## Wander: Move Operator Proposal +# If the wander operator is selected as the superoperator, and tank is not +# blocked in the forward direction, then propose the move operator. +``` + +Using what you know about the superstate augmentation, here is what you +might have come up with: + +```Soar +sp {wander*propose*move +(state ^superstate.operator.name wander +^io.input-link.blocked.forward no) +--> +( ^operator + =) +( ^name move +^actions.move.direction forward)} +``` + +Unfortunately, this rule will not match because there is no ^io +augmentation of the substate. Further, the missing io means that the +general rules you wrote to perform the action of this operator by +creating and removing the move action command on the output-link won’t +fire. One approach would be to rewrite this rule so that instead of +testing ^io.input-link… it tested ^superstate.io.input-link… However, +that doesn’t solve the other problem and if there were a deeper +hierarchy, you would have to know exactly how many levels of superstates +there were. An alternative is to copy the io augmentation down to the +state. A simple, general rule can do this and solve both problems. + +```Soar +sp {elaborate*state*io +(state ^superstate.io ) +--> +( ^io )} +``` + +This rule copies the io pointer to each new substate. You should add +this rule to your set of general rules. + +With this rule, the only change that has to be made to your operator +proposals is to add a test for: +^superstate.operator.name wander. This tests that the _selected_ +superoperator is wander. It will not match against any other _proposed_ +operators because they will have only acceptable preferences (+). An +alternative approach, which simplifies these operator proposals, and is +consistent with the convention adopted earlier for naming the top state, +is to create a rule that copies the name of the superoperator to the +state, so that your rules can directly test the name on the state: + +```Soar +sp {elaborate*state*name +(state ^superstate.operator.name ) +--> +( ^name )} +``` + +This rule is always created by VisualSoar and is in the default set of +rules, so you don’t have to add it. With these two changes, the new rule +is essentially the original, but with the name of the state being tested +changed to wander. + +Based on the analysis above, you can now rewrite the proposal rules for +the operators that apply as part of the wander operator. In order to +keep better track of your rules, you should also adopt the convention of +including the name of the state (wander) at the beginning of the name of +the rules. + +```Soar +sp {wander*propose*move +(state ^name wander +^io.input-link.blocked.forward no) +--> +( ^operator + =) +( ^name move +^actions.move.direction forward)} +``` + +```Soar +sp {wander*propose*turn +(state ^name wander +^io.input-link.blocked ) +( ^forward yes +^ { << left right >> } no) +--> +( ^operator + =) +( ^name turn +^actions ) +( ^rotate.direction +^radar.switch on +^radar-power.setting 13)} +``` + +```Soar +sp {wander*propose*turn\*backward +(state ^name wander +^io.input-link.blocked ) +( ^forward yes +^left yes +^right yes) +--> +( ^operator +) +( ^name turn +^actions.rotate.direction left)} +``` + +### General Rule Summary + +Using the rules you’ve written in this section so far, together with the +general rules, your tank should wander around until it encounters +another tank. Below is the complete set of general rules you should have +in your tank. The appropriate place to put them is in the elaborations +folder/directory. + +```Soar +sp {apply\*operator\*create-action-command +(state ^operator +^io.output-link ) +( ^actions ) +( ^ ) +( ^ ) +--> +( ^ ) +( ^ )} +``` + +```Soar +sp {apply\*operator\*remove-command +(state ^operator.actions +^io.output-link ) +( ^ ) +( ^status complete) +--> +( ^ -)} +``` + +```Soar +sp {elaborate*state*io +(state ^superstate.io ) +--> +( ^io )} +``` + +```Soar +sp {elaborate*state*name +(state ^superstate.operator.name ) +--> +( ^name )} +``` + +The trace of your tank should look something like the following, where +wander is selected, and then the move and turn operators are selected +for the substate. + +![](Images/03/image13.png) + +## Chase, Attack, and Retreat + +Remember that the hierarchy of operators that you build will look like +the following: + +In this section you will write the rules for Chase, Attack, and Retreat. +By now you should know Soar well enough to try to write these on your +own. However, some tricky issues will come up and the rest of this +section will lead you through writing them step-by-step. Even if you do +write the operators yourself, you will find it valuable to go through +the tutorial sections because they introduce a few new ideas in how to +write Soar programs. + +### Chase Operator Proposal + +The chase operator should be selected when a tank senses that another +tank is close, but it doesn’t know exactly where the other tank is. As +discussed earlier, the sound sensor gives a good indication of whether +another tank is close (if the other tank is moving), but it is not +sufficient for attacking the other tank. If a tank has another tank on +radar, it can attack and need not chase. However, if the tank has no +missiles, or is very low on energy (so it cannot use shields or radar), +it should not chase another tank. Finally, the chase operator should +only be proposed for the top state. If you put all of these together, +you will come up with the conditions for proposing the chase operator. + +```Soar +## Propose Chase Operator +# If the task is tanksoar, and sound sensor is not silent, and there +# is no tank on radar, and energy or missiles is not low, then propose the +# chase operator. +``` + +To write a Soar rule that tests these conditions requires coming up with +a definition of low health and low missiles. You could just pick a +number, such as 100, and test that the energy is not below that number, +but you will have to remember that number when you write other +operators, such as retreat, that handle the cases when the energy is +below that number. You will also include two conditions, one to test +energy and one to test missiles in every rule. A more general approach +is to create rules that test for either the energy levels being low or +no missiles, and create a structure in working memory that can be tested +in the future. + +Rules for generating these classifications should be restricted to +firing only in the top state. Otherwise, these rules will fire every +time a new state is created, because the ^io attribute is copied to all +states. It is easy to restrict this to firing only in the top state by +adding the condition: ^name tanksoar. + +```Soar +sp {elaborate*state*missiles\*low +(state ^name tanksoar +^io.input-link.missiles 0) +--> +( ^missiles-energy low)} +``` + +```Soar +sp {elaborate*state*energy\*low +(state ^name tanksoar +^io.input-link.energy <= 200) +--> +( ^missiles-energy low)} +``` + +These rules have exactly the same action – the same identifier, +attribute, and value. What happens when both rules fire at the same +time? Soar never allows duplicate working memory elements, so although +both rules fire, only one working memory element is created. + +The chase proposal then is: + +```Soar +sp {propose\*chase +(state ^name tanksoar +^io.input-link + +-^missiles-energy low) +( ^sound <> silent + +-^radar.tank) +--> +( ^operator +) +( ^name chase)} +``` + +### Chase Operator Application + +The chase operator is applied through the selection of the move and turn +operators. Both operators test the direction of the sound, which can be +accessed directly from the input-link, or to simplify the rules, you can +add an elaboration rule that copies the sound to the local state. For +example, you can add the rule: + +```Soar +sp {chase*elaborate*state\*sound-direction +(state ^name chase +^io.input-link.sound ) +--> +( ^sound-direction )} +``` + +One advantage of this approach is that if you later change the way in +which the sound direction is computed, only this rule needs to be +modified, and not the ones that test sound-direction. This is exactly +what is going to happen later when we add operators to remember sounds +in Section 5.2. + +Since the goal of the chase operator is to get a radar contact with the +enemy tank, the radar should be turned on if it is off, which can be +done in parallel with the other operators. The following rule elaborates +a selected operator with commands to turn on the radar if it is off. + +```Soar +sp {chase*elaborate*radar +(state ^name chase +^operator.actions +^io.input-link.radar-status off) +--> +( ^radar.switch on +^radar-power.setting 13)} +``` + +```Soar +## Propose Move Operator +# If the state is named chase and the sound is coming from the +# forward position, propose move forward. + +sp {chase*propose*move +(state ^name chase +^sound-direction forward +^io.input-link.blocked.forward no) +--> +( ^operator +) +( ^name move +^actions.move.direction forward)} +``` + +```Soar +## Propose Turn Operator +# If the state is named chase and the sound is coming from left or +# right, turn that direction. + +sp {chase*propose*turn +(state ^name chase +^sound-direction {<< left right >> }) +--> +( ^operator + =) +( ^name turn +^actions.rotate.direction )} +``` + +```Soar +## Propose Turn Operator Backward +## If the state is named chase and the sound is coming from backward, turn left. +sp {chase*propose*backward +(state ^name chase +^sound-direction backward) +--> +( ^operator +) +( ^name turn +^actions.rotate.direction left)} +``` + +### Attack Operator Proposal + +The purpose of the attack operator is to shoot missiles and hit the +other tank. Thus, the attack operator should be selected when a tank can +see another tank on radar. However, if a tank is low on health or +energy, it may be better to retreat instead of attack. As with chase, +the attack operator should only be proposed for the top state. If you +put all of these together, you will come up with the conditions for +proposing the attack operator. + +```Soar +## Propose Attack Operator +# If the state is tanksoar, and there is a tank on radar, and health and energy are not low, then +# propose the attack operator. +``` + +The above proposal can be written as a single rule. There should be an +indifferent preference for the operator because there can be more than +one tank on the radar and thus, more than one operator instantiation +created. + +```Soar +sp {propose\*attack +(state ^name tanksoar +^io.input-link.radar.tank + +-^missiles-energy low) +--> +( ^operator + =) +( ^name attack)} +``` + +An alternative approach that avoids the possibility of multiple +instantiations is to have an elaboration rule that detects when there is +a tank on radar and creates and augmentation of the top-state, something +like ^tank detected. Even if there are multiple tanks, only one +augmentation will be created (working memory is a set). Then a proposal +rule can be written that test that augmentation and proposes a single +attack operator. + +### Attack Operator Application + +The attack operator is applied through the selection of the move, turn, +and fire operators. + +- If an enemy is on radar in the center, the tank can just fire a + missile. +- If an enemy is off center and in front, the tank should move to the + left or right to get an open shot. +- If it can’t slide because it is blocked, the tank should move + forward. +- If the enemy is right next to the tank, it should turn. + +If you want your tank to be aggressive, it could move and shoot in one +operator. + +```Soar +## Propose Fire-missile Operator +# If the state is attack and there is a tank on radar in the center, then propose the fire missile operator. + +sp {attack*propose*fire-missile +(state ^name attack +^io.input-link ) +( ^radar.tank.position center +^missiles > 0) +--> +( ^operator + >) +( ^name fire-missile +^actions.fire.weapon missile)} +``` + +The number of missiles is tested so that the action will be attempted +only if there are missiles available. This operator needs a best +preference so that is preferred to the following operators. + +```Soar + +## Propose Slide Operator If the state is attack and there is a tank on radar +# that is not in the center, and there is not a tank in the center, and there is +# an open spot in the direction of the tank, then propose the slide operator in +# the direction of the tank. + +sp {attack*propose*slide +(state ^name attack +^io.input-link ) +( ^blocked. no +^radar ) +( ^tank.position { << left right >> } +-^tank.position center) +--> +( ^operator + =) +( ^name slide +^actions.move.direction )} +``` + +This operator must be indifferent in case there is more than one tank on +radar. + +```Soar +## Propose Move-Forward Operator +# If the state is attack and there is a tank on radar that is not in the center, and there is not a tank in the +# center, and the tank is blocked in that direction then propose move-forward. + +sp {attack*propose*move-forward +(state ^name attack +^io.input-link ) +( ^blocked. yes +^radar ) +( ^tank +-^tank.position center) +( ^position { << left right >> } +^distance <> 0) +--> +( ^operator + =) +( ^name move-forward +^actions.move.direction forward)} +``` + +The final condition tests that the opposing tank is not at distance 0, +which would be true if the opposing tank were immediately next to the +tank under control. In that case, the tank should turn and fire on the +enemy tank. That operator is proposed in the next rule. + +```Soar +### Propose Turn Operator +## If the state is attack and there is a tank on radar that right next to the tank, then propose turning in that +## direction and firing. + +sp {attack*propose*turn +(state ^name attack +^io.input-link.radar.tank ) +( ^distance 0 +^position { << left right >> }) +--> +( ^operator + =) +( ^name turn +^actions ) +( ^rotate.direction +^fire.weapon missile)} +``` + +This rule has two actions: rotating and firing a missile. In TankSoar, +both actions will happen during the same decision, but the rotate will +occur first, followed by firing the missile. This has nothing to do with +the order of the actions, but is a feature of TankSoar. + +### Retreat Operator Proposal + +The purpose of the retreat operator is to get the tank out of harm’s way +when it is low on missiles or energy. Thus, the retreat operator should +be selected when a tank has low missiles or energy and it senses that +there is another tank near. As discussed earlier, the sound and radar +indicates if another tank is close, while the incoming sensor tells if +the tank is under attack. Another situation in which the tank can +retreat is when it is under attack, but does not detect the enemy tank +so that it can attack or chase it. As with chase, the retreat operator +should only be proposed for the top state. If you put all of these +together, you will come up with these conditions for proposing the +retreat operator. + +````Soar +# Propose Retreat Operator +# If the state is tanksoar and the sound sensor is not silent or there is a tank on radar or there is an +# incoming missile, and health is low or the energy is low, then propose the retreat operator. +```Soar + +The above proposal cannot easily be written as a single rule, but can be +written as three rules. Since more than one of these conditions can be +true at a given time, these proposals should be indifferent. + +```Soar +sp {propose\*retreat\*sound +(state ^name tanksoar +^missiles-energy low +^io.input-link.sound { <> silent}) +--> +( ^operator + =) +( ^name retreat)} +```` + +```Soar +sp {propose\*retreat\*radar +(state ^name tanksoar +^missiles-energy low +^io.input-link.radar.tank) +--> +( ^operator + =) +( ^name retreat)} +``` + +```Soar +sp {propose\*retreat\*incoming +(state ^name tanksoar +^missiles-energy low +^io.input-link.incoming. yes) +--> +( ^operator + =) +( ^name retreat)} +``` + +```Soar +# Propose Retreat Operator +# If the state is tanksoar and the tank is under attack but cannot not directly sense the other tank, then +# propose the retreat operator. + +sp {propose\*retreat\*incoming\*not-sensed +(state ^name tanksoar +^io.input-link ) +( ^incoming. yes +-^radar.tank +^sound silent) +--> +( ^operator + =) +( ^name retreat)} +``` + +### Retreat Operator Application: State Elaboration + +In applying the retreat operator, the direction of the enemy is +important in deciding which way to move. That is, the tank should +attempt to move out of the line of possible fire from another tank +(side-step), or if that cannot be done, move away until a sidestep +action is possible. It is easy to determine the direction for sound, but +is subtle for radar. In radar, the general direction of the enemy is +forward, but if the enemy is off center, the tank should not move in +that direction, because that is probably right in the line of fire. In +addition, there might be multiple tanks around, some detected via sound, +some via radar, so that multiple directions need to be avoided. To +handle this, you can add rules that elaborate the substate that has been +created with the directions that enemies are detected, and directions +that should be avoided (if an enemy is off center on radar). + +In this case the following operator elaborations would be useful. + +```Soar +# Retreat Operator Elaboration +# If there is a retreat state and there is a sound coming in a given direction, record that direction. +# If there is a retreat state and there is radar contact with a tank, record forward direction. +# If there is a retreat state and there is an incoming, record the direction. +# If there is a retreat state and there is radar contact with a tank that is not in the center, record +# that direction as a direction to avoid moving. + +sp {elaborate\*retreat\*sound\*direction +(state ^name retreat +^io.input-link.sound { <> silent }) +--> +( ^direction )} +``` + +```Soar +sp {elaborate\*retreat\*radar\*front +(state ^name retreat +^io.input-link.radar.tank) +--> +( ^direction forward)} +``` + +```Soar +sp {elaborate\*retreat\*incoming\*direction +(state ^name retreat +^io.input-link.incoming. yes) +--> +( ^direction )} +``` + +```Soar +sp {elaborate\*retreat\*radar\*direction +(state ^name retreat +^io.input-link.radar.tank.position { <> center }) +--> +( ^avoid-direction )} +``` + +Many of these rules may fire at the same time, and in some cases, +attempt to create exactly the same working memory element, such as if +sound is coming from the front and there is a tank on radar in the +front. As mentioned earlier, only a single copy of an identifier, +attribute, and value will be added to working memory. However, if the +values will be different, such as if there is sound coming from the +right when a tank is on radar in front, all elements with different +values will be added to working memory. + +An important component of Soar is that if changes to working memory make +these rules no longer match, the structures they create will be removed +and recomputed as new sensor information becomes available. + +### Retreat Operator Application + +The retreat operator is applied through the selection of the move +operators. Since the goal of the retreat operator is to get away from +another tank, the move operator is selected to be a sidestep from the +direction of any sensed tank. The direction of the sidestep should not +be toward another tank, or in a direction that was recorded to avoid. If +there are no good choices available, there are a few possibilities, but +to keep things simple, just have the tank wait and be silent, and hope +the other tank goes away. + +```Soar +# Propose Move Sidestep Operator +# If the state is named retreat then propose sidestep from the direction of a detected enemy, as +# long as that direction is not blocked, is not the direction of another enemy or is a direction to avoid. +# +# Propose Wait +# If the state is named retreat then propose wait, and make a worst preference for it. +``` + +Calculating the direction for the sidestep operator is similar to some +of the calculations that needed to be performed in the Eater. As before, +you can add a data structure to working memory. This data structure has +to contain each direction, and then the two directions that are adjacent +to it. For example, the forward direction would have left and right. You +have a choice of creating this data structure for the top-level state +when the tank is created, or creating it each time that a retreat state +is generated. It is more efficient to generate it only once for the top +state; however that means you need to access it via the ^superstate +augmentation. + +```Soar +sp {elaborate\*sidestep-directions +(state ^name tanksoar) +--> +( ^side-direction ) +( ^forward right left ^backward right left +^right forward backward ^left forward backward)} +``` + +The actions of this rule have multiple values for a single attribute. +This can be written in a shorthand form without repeating the attribute. + +```Soar +sp {retreat*propose*move +(state ^name retreat +^direction +^superstate.side-direction. +-^direction +-^avoid-direction +^io.input-link.blocked. no) +--> +( ^operator + =) +( ^name move +^actions.move.direction )} +``` + +### Shield Control Rules + +Turning on shields is the main defensive mechanism available to a tank. +However, it is quite expensive in terms of energy, so it should be used +only if under attack or when the risk of attack is high. To simplify +things, you will only turn on your shields when under attack, and will +turn off the shields if not under attack. Therefore turning on the +shields is a reflex action that should happen as soon as possible. It +does not have to be open to deliberate consideration, and therefore can +be encoded as a rule instead of an operator. In a later section, you +will learn how to use Soar’s learning mechanism so that these can be +written as operators, but then be compiled into rules. + +How should the rule for turning the shields on be written? The obvious +choice is to test the incoming sensor and directly modify the +output-link; however, adding directly to the output link can cause a +subtle problem. The problem arises when tanks are used in competitive +tournaments with the “Run Until Output Generated” flag set. This flag is +set in the Tank Control Window, under the Tanks menu item. When set, +each tank is run until it has generated output (with a limit of fifteen +decisions). Usually a tank will run only a single decision, but if the +tank is changing between wandering and attacking, a few decisions might +be made before output is generated. However, if shields are turned on +and off via actions directly to the output-link, output will be +available during the first decision. In some cases, such as changing +from wander to attack, another action could have been generated if the +shield command waited until other output was ready. The simplest way to +do this is have the shield commands piggyback on other operators that +have actions. Each rule tests that the operator has an ^actions +augmentation and adds its command. + +```Soar +sp {elaborate\*shields-on +(state ^operator.actions +^io.input-link ) +( ^incoming. yes +^shield-status off) +--> +( ^shields.switch on)} +``` + +```Soar +sp {elaborate\*shields-off +(state ^operator.actions +^io.input-link ) +( -^incoming. yes +^shield-status on) +--> +( ^shields.switch off)} +``` + +### Wait Operator + +There is one more operator that is included in the simple tank. This is +the wait operator that is in the all folder/directory, and is useful in +almost every task, not just controlling toy tanks. The wait operator is +proposed whenever there is a state no-change impasse. A state no-change +impasse arises when no operators are proposed. This can happen because +of a bug in your program. If there were no wait operator, Soar would +generate a cascade of state no-change impasses. The wait operator is +proposed whenever there is a state no-change. + +```Soar +# Propose wait for a state-no-change + +sp {top-state*propose*wait +(state ^attribute state +^choices none +-^operator.name wait) +--> +( ^operator +) +( ^name wait)} +``` + +This proposal tests that the state has ^attribute state and ^choices +none. Those attributes on the state are created when no operator can be +selected for a state – a state no-change impasse. The key to the wait +operator is that it tests to see that a wait operator is not selected. +Thus, once one is selected, the proposal no longer matches and retracts, +but then will get reproposed during the next proposal cycle. + +### Review of Simple Tank + +After writing your tank, it is a good idea to take a step back and do a +little analysis to make sure that your have operators to cover all of +the situations your tank can get into. In this section, we will examine +the high-level operators, but you should also examine the sets of +operators used to implement each of these operators. Here is a list of +all of the conditions covered by the different operator proposals + +Wander: 1. sound silent, no tanks on radar, no incoming missile +Attack: 2. not missiles-energy low, tank on radar +Chase: 3. not missiles-energy low, not sound silent, no tank on radar +Retreat: 4. missiles-energy low, not sound silent +5\. missiles-energy low, tank on radar +6\. missiles-energy low, incoming missile +7\. sound silent, no tanks on radar, incoming missile + +We can map these on to a decision tree that successively splits on +different conditions and see if all of the leaves of the tree are +covered. This is a brute-force approach and is not practical when there +are a large number of different condition elements being tested. + +The conditions include: sound, tanks on radar, incoming missiles, and +missiles-energy low. Therefore we expect to have a four-level tree with +sixteen leaves. Although we have only seven rules, these may cover all +sixteen leaves because many rules don’t test all four conditions. To +build the tree, I picked the following condition ordering: +missile-energy, sound silent, tank on radar, and incoming missiles. Each +branch in the tree is labeled with the rules that can potentially match +it. The letters in the nodes signify which operator will be selected +(a=attack, c=chase, r=retreat, w=wander). The complete tree does not +have to be generated if all of the remaining rules for a node propose +the same operator and one of them does not test any additional features. +For example, (missiles-energy low, not-sound silent) is covered by rules +4, 5, and 6, and rule 4 does not test any additional conditions. + +### Trace of a Simple Tank + +When all of the rules have been put together, you will have a tank that +can wander, chase, attack, and retreat. If you have two of these fight +against each other, you might get a trace like the following: + +One of the things you immediately notice is that when the tank decides +to chase another tank it gives up on the next decision and goes back to +wander. This is because the other tank is probably doing the same thing +and stops moving for one decision, and so the sound sensor goes to +silent. However, once a tank does see another, it fires a lot of +missiles. Both of these should give you ideas for improving your tank. +The next section explores how to avoid the problem with the disappearing +sound. + +![](Images/03/image14.png) + +## Improving Sound Detection + +The most obvious problem with your tank is that it loses track of other +tanks whenever the other tanks stop moving. To eliminate this problem, +you can modify the rules in your tank so that when there is a sound, the +tank creates a persistent memory of the direction of that sound so that +it can chase the other tank, even if the other tank stops moving. Adding +the persistent data structure requires adding new operators and changing +all of the rules that test for sound directly from the input-link. +Because of these extensive changes, you probably want to save you +current tank and create a copy of it to modify. All of the changes in +this section are incorporated in the Tutorial/simple-sound-bot agent. + +Even if you are not interested in improving your tank, you should read +this section because it covers some of the complexities in Soar that +arise when creating persistent data structures in subgoals. + +To create and remove the sound data structure requires at least two new +operators. + +- Record-sound: create the sound data structure when a new sound is + heard. +- Remove-sound: remove the sound data structure if the direction of + sound changes or if the recorded sound has expired. + +You can write an operator to directly update the sound data structure if +it changes, instead of removing it and adding a new one, but the +approach taken here will minimize the number of rules you need to write. + +The first step in adding these operators is to determine where sound is +used and when it needs to be recorded. Sound affects the selection of +the wander, retreat, and chase operators, so any persistent structure +must be available on the top state. Sound needs to be recorded during +wander, where it triggers a shift to chase. Thus you should add +record-sound to the wander operator. Remove-sound should be selected if +a sound changes directions or becomes out of date, no matter which +top-level operator is selected. Thus, you should create +remove-sound.soar in the all folder/directory. + +### Record-sound Operator + +Proposing the record operator must test that there is a sound on the +input-link. + +```Soar +sp {wander*propose*record-sound +(state ^name wander +^io.input-link.sound <> silent) +--> +( ^operator + > =) +( ^name record-sound)} +``` + +A best preference is used so that it will be immediately selected so the +sound can be recorded. + +The rule to apply the operator must test that record-sound is the +current operator and the direction of the sound. It should also test the +current clock so that it can compute a time at which the sound +“expires”, meaning that the sound should be removed. I use 5 because +it is long enough for the tank to turn around and get its radar on to +hunt for the other tank. Finally, the rule needs to match the superstate +because sound data structure is going to be added to it. The sound +structure must be added to the superstate because the substate may +disappear, destroying all structures attached to it. Below is an initial +version of the apply rule. + +```Soar +sp {wander*apply*record-sound +(state ^operator.name record-sound +^io.input-link +^superstate ) +( ^sound +^clock ) +--> +( ^sound ) +( ^direction +^expire-time (+ 5))} +``` + +One problem with this rule is that it records the direction of the sound +in terms of forward, left, right, or backward (that is what the sound +sensor gives). This becomes a problem if the tank decides to turn and +face in the direction of the sound (the tank will seem to chase its +tail). A better approach is to record the absolute direction of the +sound: north, east, south, or west. You can add an elaboration rule that +computes the relative direction of the sound (forward, right, backward, +left) in chase where it is needed. + +To compute the absolute direction requires creating a data structure in +working memory that maps relative to absolute directions. This can be +matched using the tanks current absolute direction (given on the +direction input-link structure), and the relative direction from the +sound input-link structure. + +```Soar +sp {elaborate\*directions +(state ^superstate nil) +--> +( ^direction-map ) +( ^north +^south +^west +^east ) +( ^right east ^left west ^backward south ^forward north) +( ^right west ^left east ^backward north ^forward south) +( ^right north ^left south ^backward east ^forward west) +( ^right south ^left north ^backward west ^forward east)} +``` + +The direction-map structure provides a mapping between the current +absolute direction, a relative direction, and a new absolution +direction. For example, if a tank is facing north and the relative +direction is left, then ^direction-map.north.left has value west. + +```Soar +sp {wander*apply*record-sound +(state ^operator.name record-sound +^io.input-link +^superstate ) +( ^sound +^direction +^clock ) +( ^direction-map.. ) +--> +( ^sound ) +( ^absolute-direction +^expire-time (+ 5))} +``` + +Although the absolute direction of the sound is recorded, when chasing a +tank, the relative direction of the sound is more useful. Since it is +only needed in the implementation of chase, it only needs to be computed +there. You may remember that all of the rules that propose operators to +apply chase do not directly test the direction of the sound on the +input-link. Instead, the input-link.sound was copied onto the chase +state. Thus, to use the recorded sound, all you have to do is replace +the elaboration rule in chase/elaborations.soar with one that computes +the relative direction of the sound instead of copying the sound from +the input-link. The direction-map structure can be used to do this +calculation, which involves going from the current absolute direction of +the tank, the absolute direction of the sound, to a relative direction +of the sound. For example, if the tank is facing north, and the sound is +known to be west, you can match: `direction-map.north. west`, +where `` would match left. + +```Soar +sp {chase*elaborate*state\*sound-direction +(state ^name chase +^superstate ) +( ^sound.absolute-direction +^direction-map.. +^io.input-link.direction ) +--> +( ^sound-direction )} +``` + +### Remove-sound Operator + +The sound data structure will persist on the top-state until it is +removed; therefore, another operator must be created to remove it. This +operator should be proposed whenever the time has expired. This could +happen in almost any substate, so it is wise to store this operator in +the all directory. + +Testing that the time has expired requires comparing the time stored on +the sound data structure to the current clock. The proposal is simply: + +```Soar +sp {all*propose*remove-sound +(state ^name << wander chase retreat attack >> +^superstate.sound.expire-time +^io.input-link.clock > ) +--> +( ^operator + =, >) +( ^name remove-sound)} +``` + +One might be tempted to not even test for the name of the state in the +proposal given that it can apply in every state. However, testing the +substate name indirectly influences the persistence of the result of the +substate. This is a complex explanation that starts with the action of +remove-sound, which is to create a reject preference for the sound data +structure _on the top-state._ Although this action is part of the +remove-sound operator, it is also a result of the substate/impasse +created when a high-level operator was selected (wander, chase, retreat, +or attack). When results are created, Soar computes their persistence +(o-support or i-support) based on how they contribute to problem solving +_at the level to which they are returned._ In Soar, this involves +determining if the result is part of applying an operator at that level, +which requires determining if an operator at that level was tested in +generating the result. + +Soar determines which working memory elements were tested by recursively +tracing back from the result, through all of the rules that fired in the +subgoal and created structures that were tested in generating the +results. The figure below shows a trace of the problem solving in the +subgoal where elaborate*state*operator\*name tests (S1 ^operator O7) +and (O7 ^name chase) in creating (S8 ^name chase). Additional rules +fire, testing the working memory elements, some of which exist in the +superstate, and some which exist in the substate. Finally (S1 ^sound S5 +-) is generated as a result. The trace starts with the result and +proceeds backward, picking up all of the working memory elements from +the superstate that were tested along the way. These are all underlined +in the figure. If additional rules fired in the substate, but did not +create results that contributed to the creation of (S1 ^sound S5 -), +they would not be included in the trace. + +(S1 ^operator O7) + +(O7 ^name chase) +(S8 ^name chase) + +From this trace, Soar generates a _justification_, which is essentially +a rule that tests data structures that were in working memory before the +impasse arose, and whose action is the result. The justification does +not have any variables and includes tests for specific identifiers in +working memory. (Soar’s chunking mechanism replaces the identifiers in +justifications with constants to build new rules.) In this case the +justification would be: + +```Soar +(S1 ^operator O7) +(O7 ^name chase) +(S1 ^sound S5) +(S5 ^expire-time 20) +(S1 ^io I1) +(I1 ^input-link I2) +(I2 ^clock 21) +--> +(S1 ^sound S5 -) +``` + +The justification is analyzed to determine if the result should get +o-support or i-support. If the justification tests the operator, as it +does in this case, then the result gets o-support. If the justification +does not test the operator, the result gets i-support. If a result gets +i-support, it is removed from working memory when any of the elements +tested in the conditions of the justification are removed from working +memory. + +Getting back to remove-sound, if the proposal for remove-sound does not +test the name of the substate, the test for the operator in the +superstate would not be included, making it independent of any operator +application and the result would get i-support. However, if the proposal +for remove-sound tests the name of the substate, the result is dependent +on the selection of the operator, and the result will get o-support. + +This aspect of Soar is pretty complex and might have you wondering if +you will be able to figure out how to create results with the right +persistence. In general, you will find that all of the results you +create in operator application substates should be o-supported, and they +will as long as all of the rules that propose operators in the substate +test the name of the substate. If you ever have an operator that should +apply in a subgoal, but create an i-supported result, all you need to do +is not test the name of the substate. + +Applying remove-sound is pretty straightforward. All that needs to be +done is remove the sound data structure from the top-state, which in +this case is the superstate. + +```Soar +sp {all*apply*remove-sound +(state ^operator.name remove-sound +^superstate ) +( ^sound ) +--> +( ^sound -)} +``` + +With just the record-sound and remove-sound operators, your tank will +now correctly maintain sound even when another tank stops moving. To +take advantage of this requires modifying the proposal conditions for +wander, retreat, and chase. For example, the original proposal for chase +was: + +```Soar +sp {propose\*chase +(state ^name tanksoar +^io.input-link +-^missiles-energy low) +( ^sound <> silent + +-^radar.tank) +--> +( ^operator.name chase)} +``` + +The test for sound not being silent on the input-link is replaced for a +test that the sound data structure exists, which involves just testing +that the sound attribute is on the state. + +```Soar +sp {propose\*chase +(state ^name tanksoar +^sound +-^io.input-link.radar.tank +-^missiles-energy low) +--> +( ^operator.name chase)} +``` + +You may wish to refine the management of the sound data structure so +that if the direction changes on the input-link, the sound data +structure is removed. This is done by adding another proposal rule for +remove-sound. This rule needs to match against the current sound +direction, which is relative, and the saved sound direction, which is +absolute, so some conversion is necessary. + +```Soar +sp {all*propose*remove-sound\*changed-direction +(state ^name << wander chase retreat attack >> +^superstate +^io.input-link ) +( ^sound.absolute-direction +^direction-map.. ) +( ^sound { <> silent <> } +^direction ) +--> +( ^operator + =, >) +( ^name remove-sound)} +``` + +A further refinement you can make is to add an operator that updates the +direction of the sound instead of removing the sound data structure. + +## Creating a Map + +As your tank wanders around, it can build a map of its world that it can +later use to find the chargers or better control its radar. In this +section, you will learn how to create a map. This section is less of a +tutorial than an explanation of the code in the mapping-bot. The +mapping-bot continually builds up the map no matter which operator is +selected at the top level. The mapping-bot is the same as the +simple-sound-bot, but with additional code. + +### The Map Data Structure and Initialization + +The map is a structure that must be stored and updated while the tank is +playing the game. In the mapping-bot the map is stored in working memory +as large graph structure off the top state. This approach is not the one +to take to model human processing, but it is a straightforward approach +for building a tank. The map consists of a 16 x 16 grid of squares. The +size is 16 x 16 instead of 14 x 14 because it includes the borders as +well as the spaces where the tank can move. The map is generated by the +init-map operator, which is proposed and selected at the beginning of a +run. It is proposed with a _best_ preference, so that it will be +preferred over the other operators. Once init-map is selected, it +creates the map structure: + +```Soar +sp {apply\*init-map +(state ^operator.name init-map) +--> +( ^map ) +( ^square …) +( ^x 0 ^y 0) +( ^x 0 ^y 1) +( ^x 0 ^y 2) + +…} +``` + +Each square is initialized with its x and y location, which is the +minimal information required to distinguish between the squares. +Additional information and structure is required so that it is easy to +access squares that are adjacent to the current square, and so it is +easy to add information to the squares, such as that a square contains a +recharger, tank, missile, or an obstacle, or that the square is clear. +To make access of adjacent squares easier, we need to compute which +squares are adjacent and then add augmentations between squares. The +north and south adjacent squares have the same x value, but differ in y +by one. Similarly the west and east adjacent squares have the same y +value but differ in x by one. Thus, by adding one to the x value of +every square we can find the square to the east, and by adding one to +the y value, we can find the square to the south (we assume 0, 0 is in +the upper left (north west) corner). We can then create east-west and +north-south links between pairs of states. That is exactly what the +following two rules do for east-west (there is a similar pair for +north-south). + +```Soar +sp {top-ps*apply*init-map\*add-adjacencies\*east +(state ^operator.name init-map +^map.square ) +( ^x { < 15 }) +--> +( ^east-x (+ 1))} +``` + +```Soar +sp {top-ps*apply*init-map\*add-adjacent\*link\*east +(state ^operator.name init-map +^map.square ) +( ^y ^east-x ) +( ^y ^x ) +--> +( ^east ) +( ^west )} +``` + +Another initialization that is done at this time is to mark all of the +squares that are on the border as obstacles. This is done by matching +all squares with x or y coordinates of 0 or 15 and then adding the +obstacle \*yes\* augmentation to them. Below is an example for the x +with value 15: + +```Soar +sp {map\*mark-obstacle\*x15 +(state ^operator.name init-map +^map.square ) +( ^x 15) +--> +( ^obstacle \*yes\*)} +``` + +So during init-map, the map is created and then all of the squares are +linked via north, south, east, and west attributes. Needless to say, +there are a large number of additions to working memory during the +application of init-map. + +### Updating the Map + +In order to use and update the map, the agent must know which square on +the map it is at. The easiest way to do this is to match the agent’s +current x, y location from the input-link to all of the squares and then +create a link (named ^square) off the top state to the one that matches. +This is a simple elaboration that is retracted and refired each time the +agent moves. + +```Soar +sp {all\*map\*current-square +(state ^name tanksoar +^io.input-link +^map.square ) +( ^x +^y ) +( ^x +^y ) +--> +( ^square )} +``` + +Once there is a current square, the next step is to update the internal +map with any information that is available from the radar. This is a bit +tricky because the radar gives information relative to the agent and +does not give the absolute x, y coordinates of objects that are seen. +For example, if there is a tank detected on radar, its position will be +given as a distance (in the direction the agent is facing), and either +left, right, or center, meaning that it is either in line (center) or +one space left or right of center. A rule must translate from that type +of information into x, y. This can be done by creating a data structure +in working memory that contains the right information for changes in x +and y for each possible heading of the agent. In the mapping-bot, it is +the radar-map data structure and it is created in elaborations/top-state +as part of the elaborate\*directions rule. Below is the structures that +are created as part of radar-map: + +```Soar +--> +( ^radar-map ) +( ^north +^south +^west +^east ) +( ^center ^right ^left ^sx 0 ^sy -1) +( ^center ^right ^left ^sx 0 ^sy 1) +( ^center ^right ^left ^sx -1 ^sy 0) +( ^center ^right ^left ^sx 1 ^sy 0) +( ^x 0 ^y 0) +( ^x 1 ^y 0) +( ^x -1 ^y 0) +( ^x -1 ^y 0) +( ^x 1 ^y 0) +( ^x 0 ^y -1) +( ^x 0 ^y 1) +( ^x 0 ^y 1) +( ^x 0 ^y -1)} +``` + +For each direction, there is a substructure. For that substructure, +there are center, left, and right objects, each of which contains +displacements for x and y so that the coordinate can be computed +correctly. In addition, get direction object contains an sx and sy +augmentation that is used via multiplication to determine the +displacement of the distance from the agent’s current position. If the +agent is facing north or south, sx is 0, so there is no x component to +the distance, and the sy is either 1 or –1 depending on whether the +agent is facing south or north. + +This structure is then used in rules that compute the x, y location of +objects seen on radar. + +```Soar +sp {map\*mark-object +(state ^name tanksoar +^map +^io.input-link +^square +^radar-map. ) +( ^ +^sx +^sy ) +( ^x +^y ) +( ^radar.{ + +<< health energy obstacle open missiles tank >> } +^direction ) +( ^distance +^position ) +( ^x +^y ) +--> +( ^ ) +( ^x (+ (+ (\* )) ) +^y (+ (+ (\* )) ))} +``` + +This creates a temporary (I-supported) structure on the map for each +object seen on radar along with the x, y location. + +Following this, an additional rule can then match this structure with +the squares in the map and update the map structure. This rule must make +a persistent change to the map, and thus must either be part of an +operator, or must use the special notation of :o-support following the +name of the rule. In the rules below, :o-support is used just to +illustrate its use. You could also include a test that an operator is +selected `(^operator.name )`, because this should be done no +matter which operator is selected. In general, using the :o-support is +not a good idea and it is easy to get carried away with it, so use it +with caution. + +```Soar +sp {map\*record-object + +:o-support +(state ^name tanksoar +^map ) +( ^{ << obstacle health energy open tank missiles >>} + +^square ) +( ^x ^y ) +( ^x ^y ) +-( ^ \*yes\*) +--> +( ^ \*yes\*)} +``` + +This marks squares with augmentations such as ^health \*yes\*, meaning +there is a health charger on that square. + +Although obstacles and chargers are permanent, the missiles and tanks +are not. Therefore, rules must be added that remove these when a square +is detected to be open but not have missiles on them. The test for open +is included to make sure that the radar is on and that there is +something detected in that location. Otherwise, the missile would be +removed whenever no missiles are detected for that square, even if it is +impossible for the tank to see the square at the current time. + +```Soar +sp {map\*clean\*missiles +:o-support +(state ^name tanksoar +^map ) +( ^square +^open ) +( ^x +^y +^missiles \*yes\*) + +-{( ^missiles ) +( ^x +^y )} +( ^x +^y ) +--> +( ^missiles \*yes\* -)} +``` + +```Soar +sp {map\*clean\*tank +:o-support +(state ^name tanksoar +^map ) +( ^square +^open ) +( ^x +^y +^tank \*yes\*) + +-{( ^tank ) +( ^x +^y )} +( ^x +^y ) +--> +( ^tank \*yes\* -)} +``` + +Furthermore, even if the tank doesn’t have its radar on, it can remove +tanks and missiles from the square that the tank occupies: + +```Soar +sp {map\*clean\*missile\*occupied +:o-support +(state ^name tanksoar +^square ) +( ^missiles \*yes\*) +--> +( ^missiles \*yes\* -)} +``` + +```Soar +sp {map\*clean\*tank\*occupied +:o-support +(state ^name tanksoar +^square ) +( ^tank \*yes\*) +--> +( ^tank \*yes\* -)} +``` + +We can also mark the current square to be open, as well as mark it with +a charger if one is detected. + +```Soar +sp {map\*record-open\*there +:o-support +(state ^name tanksoar +^square ) +( -^open \*yes\*) +--> +( ^open \*yes\*)} +``` + +```Soar +sp {map\*record-energy\*there +:o-support +(state ^name tanksoar +^io.input-link.energyrecharger yes +^square ) +( -^energy \*yes\*) +--> +( ^energy \*yes\*)} +``` + +```Soar +sp {map\*record-health\*there +:o-support +(state ^name tanksoar +^io.input-link.healthrecharger yes +^square ) +( -^health \*yes\*) +--> +( ^health \*yes\*)} +``` + +### Using the Map + +The map can be used in a variety of ways. The most obvious is to control +the tank from the map instead of from radar, so that the tank only moves +to squares that are open on the map and it tries to pick up missiles +from the map. Thus, it can turn off the radar as soon as it sees some +missiles. + +A more complex use of the map is to store in each square the distance in +each direction to the first obstacle that would block the radar. In the +future, this value can be used to control radar-power, thus minimizing +the energy drain of the radar. + +The map can also be used to create paths to the chargers so that when a +tank is low on health or energy, and has previously seen a charger, it +can return to the charger. This can be a bit more challenging to +implement, but it can be done. + +## Improving Your Tank + +There are many ways you can modify your tank to improve its performance. +The best way to do this is to watch its behavior very carefully and +continually ask yourself, “What would I do if I were the tank?” + +### Possible Extensions + +There are many possible extensions to your tank. Here are the ones that +I’ve thought of, in increasing order of difficulty: + +1. The tank picks up missiles that it sees on radar. + +2. The tank recharges at the health or energy chargers when it sees one + on radar and is low on health or energy. + +3. Radar-power is set to be only as far as the border. + +4. The tank uses different attack tactics based on how many missiles it + has (aggressive if it has lots of missiles, more conservative if it + is low on missiles). + +5. The tank uses the rwaves sensor better. + +6. The tank has more sophisticated wandering. + + 1. Turns toward the length of hall before it moves into hall so it + does not expose itself to possible attack while turning. + + 2. Avoids getting stuck inside of rooms. + + 3. Takes more advantage of the structure of the maps. + +7. The tank has more sophisticated attacking + + 1. Tries to draw enemy fire and then get out of the way. + + 2. Better attacking when radar isn’t on. + + 3. Better movement to attack when there are obstacles between your + tank and the enemy. + +8. The tank uses the map to: + + 1. Move directly to chargers when low. + + 2. Find places to hide when retreating or attacking. + + 3. Turn on radar so that it will reach only as far as any blocking + obstacle. + +You will find that a tank that fights well and can keep moving even when +it runs out of energy will do much better than a complex tank that does +mapping and has other tactics but sometimes “goes dumb” (starts to +thrash between operators or gets a tie between operators). + +### Final Thoughts + +There are some other important aspects of substates in Soar not covered +in this tutorial, and you should study the Soar 8 manual to learn about +them. For example, Soar automatically terminates a substate if there are +o-supported structures in the substate whose bases for creation in the +superstate changes. Also, Soar has a learning mechanism, called +chunking, that can be used to automatically learn new rules. + +Some general advice to keep you out of trouble is: + +- Never combine different problem solving functions in a single rule. + For example, do not create a rule that both applies one operator and + simultaneously proposes another operator. +- Don’t propose two operators in a single rule. +- Don’t combine operators that create internal persistent structures. + This isn’t as important as the other two; however, by keeping each + operator separate, it will be much easier to debug your code. diff --git a/docs/tutorials/soar_tutorial/04.md b/docs/tutorials/soar_tutorial/04.md new file mode 100644 index 00000000..6a6fd1a1 --- /dev/null +++ b/docs/tutorials/soar_tutorial/04.md @@ -0,0 +1,876 @@ +# Part IV: More Simple Problem Solving + +This part of the tutorial returns to problems that are solved through +internal search. You will build Soar programs for a classic AI problem: +Missionaries and Cannibals. Other classic AI problems, including Blocks +World, the Eight Puzzle, and Towers of Hanoi are included in the set of +demonstration programs that comes with the Soar release. This is very +similar to the water jug problem you did initially, but has a few +interesting extensions. You will start by building the operators, state +descriptions, and goal tests that are required to define each problem. +You will also be introduced to more of the theory of problem solving +based on search in problem spaces. In Part V, you will learn how to +modify and extend programs so that they use planning and learning to +solve problems. + +This problem is challenging in a different way than playing games such +as Eaters and TankSoar. Eaters and TankSoar are competitive games and +they require fast intelligent responses to the current situation, which +can change quickly. This problem does not have dynamic environments. +However, solving this problem requires selecting the one appropriate +operator from a set of many at each decision point. Selecting the +correct operator is not easy given the knowledge available from the +problem description. The problem can be solved only through trial and +error, which involves searching through the space of possible states. + +## Missionaries and Cannibals Definition + +Problem Statement: + +> Three missionaries and three cannibals come to a river. There is a boat +> on their bank of the river that can be used by either one or two persons +> at a time. This boat must be used to cross the river in such a way that +> cannibals never outnumber missionaries on either bank of the river +> (although cannibals can be alone on one bank). How do the missionaries +> and cannibals successfully cross the river? + +Once again, the first step to creating a Soar program to solve this +problem is to decompose it into the problem space (state representation +and operators) and the problem (initial state and desired state). One +interesting aspect of this problem is that it also includes failure +states. If the cannibals ever outnumber the missionaries, then you have +failed. Below is a partial graph (it doesn’t show the last four steps) +of the problem space, which shows that there are many illegal states +that need to be avoided along the way to a solution. + +![](Images/04/1.png) + +Below is a list of the aspects of the problem space and problem that you +will define for this problem: + +1. The state representation. For this problem this will include the + positions of the missionaries, cannibals, and boat, relative to the + river. +1. The initial state creation rule. In this problem, all the + missionaries, cannibals and the boat are on one bank of the river. +1. The operator proposal rules. For this problem the operators move up + to two of the missionaries and/or cannibals across the river with + the boat. +1. The operator application rules. +1. The operator and state monitoring rules. +1. The goal recognition rule. In this problem, the desired state is + achieved when all missionaries and cannibals have crossed the river. +1. The failure recognition rule. These are rules that detect when a + state is created in which the goal cannot be achieved. In this + problem, the failure states are whenever the cannibals outnumber the + missionaries on one bank of the river. +1. The search control rules. + +It may be tempting to try to incorporate the avoidance of failure states +into the operators, so that operators are never proposed that lead to +failure states. However that is moving an aspect of the problem into the +problem space and requires some problem solving to determine what the +conditions of the proposal should be. We will see in Part V how Soar can +learn to rules that avoid proposing operators when they will lead to +failure. + +As in the Water Jug problem, this program does not create a plan to +solve the problem. Instead, when the program finishes, all of the +missionaries and cannibals will have been moved across the river. + +## State Representation + +What are the parts of the problem that must be represented on the state? +Everything in the problem description is important (there are no +irrelevant objects are characteristics of the objects), so an initial +list of objects includes three missionaries, three cannibals, a river, +and the boat. + +At any point in solving the problem, it is necessary to represent which +bank of the river the boat is and the banks that the missionaries and +cannibals are on. One important observation is that it is not necessary +to keep track of each missionary and cannibal individually. All that is +important is the _number_ of missionaries and cannibals on each bank of +the river. For the purposes of this problem all cannibals are the same +and all missionaries are the same. Therefore, it is not necessary to +have a separate representation of each missionary or cannibal and its +current position. Another observation is that you never have to +represent a state where the boat has missionaries and cannibals in it – +that happens only during the application of an operator. The only states +that need to be represented are those with the boat on one bank of the +river or the other. Therefore the important aspects of the states that +need to be represented are: + +- The number of missionaries on each bank of the river. +- The number of cannibals on each bank of the river. +- The bank of the river that the boat is on. + +There are many possible ways to represent this information using Soar’s +attributes and values. Try to come up with one on your own before +looking at the representations listed below. + +In creating the representations below, the two banks of the river are +named left and right, with left being the bank of the river the +missionaries and cannibals start out on. + +Here is one possible representation: + +```Soar +(state ^right-bank-missionaries 0-3 + ^left-bank-missionaries 0-3 + ^right-bank-cannibals 0-3 + ^left-bank-cannibals 0-3 + ^right-bank-boat 0/1 + ^left-bank-boat 0/1) +``` + +Although this representation is adequate for solving the problem, it +doesn’t allow you to write general rules for proposing and applying +operators. Using this representation, you would have to write separate +proposal and application rules for when the boat is on each bank of the +river. You would also have to write separate rules for moving cannibals +and missionaries. When you have an attribute like right-bank-boat, the +rules in Soar cannot match the different substructures, such as +right-bank and boat. By representing each aspect separately using +structured objects, you will find that it is possible to write very +general operator proposal and application rules. + +There are two obvious structured representations to choose from. One +state representation has objects on the state for the two banks of the +river, with subobjects for the missionaries, cannibals, and boat on that +bank. To simplify later processing, an additional attribute (other-bank) +can be added to the subobjects so that opposite bank can be matched +easily. Below is a representation for the initial state: + +```Soar +(state ^left-bank + ^right-bank ) + ^cannibals 3 + ^boat 1 + ^other-bank ) + ^cannibals 0 + ^boat 0 + ^other-bank ) +``` + +![](Images/04/2.png) + +An alternative is to make the missionaries, cannibals, and boat the +primary way of structuring the data, with the number of entities on each +bank of the river as secondary. + +```Soar +(state ^missionaries + ^cannibals + ^boat ) +( ^left 3 + ^right 0) + ^right 0) +( ^left 1 + ^right 0) +``` + +![](Images/04/3.png) + +For this problem, both of these representations are sufficient and they +are similar in terms of the ease of writing the operators and goal +tests. Soar programs for both are included as demonstration programs +with the Soar release. For the remainder of this section, the first +representation will be used because it is closer to the physical +structure of the problem. + +## Initial State Creation: Initialize-mac + +Just as in the Water Jug problem, you should create an initialization +operator that names the state and creates all of the initial features on +the state. + +````Soar +```Soar +sp {mac*propose*initialize-mac + (state ^superstate nil + -^name) +--> + ( ^operator +) + ( ^name initialize-mac)} +```` + +```Soar +sp {mac*apply*initialize-mac + (state ^operator.name initialize-mac) +--> + ( ^name mac + ^left-bank + ^right-bank + ^desired ) + ( ^missionaries 0 + ^cannibals 0 + ^boat 0 + ^other-bank ) + ( ^missionaries 3 + ^cannibals 3 + ^boat 1 + ^other-bank ) + ( ^right-bank ) + ( ^missionaries 3 + ^cannibals 3 + ^boat 1)} +``` + +## Operator Proposal + +The operators for this task move 1 to 2 individuals (missionaries or +cannibals) across the river. In writing the proposal rules, it is +easiest to break the operators into three classes: + +- move one missionary or cannibal to the other bank +- test that there is at least one of the given type on the bank with + the boat +- move two missionaries or two cannibals +- test that there is at least two of the given type on the bank with + the boat +- move one missionary and one cannibal together +- test that there is at least one of each type on the bank with the + boat + +Try to write an English description of the proposal for first operator. + +``` +mac*propose*move-mac-boat*1 +``` + +If the name of the state is mac and there is one or more cannibal or +missionary on the same bank of the river as the boat, then propose +moving that cannibal or missionary across the river. + +The other operator proposals are very similar: + +``` +mac*propose*move-mac-boat*2 +``` + +If the name of the state is mac and there are two or more cannibals or +missionaries on the same bank of the river as the boat, then propose +moving two of that type across the river. + +``` +mac*propose*move-mac-boat*1 +``` + +If the name of the state is mac and there is one or more cannibal and +one or more missionaries on the same bank of the river as the boat, then +propose moving one cannibal and one missionary across the river. + +As in the Water Jug problem, you need to decide on a representation of +the operator and its parameters. For this task, the operator parameters +that make sense are: + +- The name of the operator: move-mac-boat. +- The type of entities being moved: cannibal or missionary. +- The number of each type of entity being moved: 1 or 2. + +The second two can be combined as a single attribute-value pair, with +the type of entity being the attribute and the number being the value. +This makes it easy to represent moving one or two entities of the same +type as well as moving one missionary and one cannibal. To simplify +later matching, you can also include the bank of the river that the boat +is on. Also, for some of the reasoning, it will be useful to also +represent how many different types of people are being moved, usually +just one, but two when moving one missionaries and one cannibal. It is +not necessary to include the bank of the river that the boat is on +because it is represented in the current state. + +The operator representation for moving one cannibal (with the boat) from +the bank with object l3 would be: + +``` +( ^name move-mac-boat + ^cannibal 1 + ^boat 1 + ^bank l3 + ^types 1) +``` + +Now try to write the first proposal as a Soar rule. To make it easier, a +good approach is to initially write a very specific rule for one type of +operator, and then attempt to generalize it by adding variables. To get +started, you can try writing a proposal rule for just cannibals on the +left bank of the river. That would be: + +````Soar +```Soar +sp {mac*propose*move-mac-boat1 + (state ^name mac + ^left-bank ) + ( ^cannibals > 0 + ^boat 1) +--> + ( ^operator + =) + ( ^name move-mac-boat + ^bank + ^boat 1 + ^cannibals 1 + ^types 1)} +```` + +The operator is created with both an acceptable and a indifferent +preference. In a later section you will explore adding search control. + +You can generalize this rule by using a variable for the bank of the +river, making it so that the proposal applies no matter which bank the +boat is on. To be safe, this requires introducing a disjunctive +`(<< left-bank right-bank >>)` test for the attribute. + +````Soar +```Soar +sp {mac*propose*move-mac-boat*1 + (state ^name mac + ^<< right-bank left-bank >> ) + ( ^cannibals > 0 + ^boat 1) +--> + ( ^operator + =) + ( ^name move-mac-boat + ^bank + ^boat 1 + ^cannibals 1 + ^types 1)} +```` + +You can then further generalize the rule so that it can match against +both cannibals and missionaries (but not the boat). This requires +introducing a disjunctive `(<< cannibals missionaries >>)` test for +the attribute of the bank object, and also a surrounding conjunctive +test `({ << cannibals missionaries >> })` to match the entity +type to the variable ``, which can then be used in the action. The +final rule is: + +````Soar +```Soar +sp {mac*propose*operator*move-mac-boat1 + (state ^name mac + ^<< right-bank left-bank >> ) + ( ^{ << cannibals missionaries >> } > 0 + ^boat 1) +--> +( ^operator + =) +( ^name move-mac-boat + ^bank + ^ 1 + ^boat 1 + ^types 1)} +```` + +Now try to write the second proposal that moves two missionaries or two +cannibals as a Soar rule. This requires only minimal changes to the +first. The only changes are to test for more than one missionary or +cannibal, and to increase the number being moved to 2. + +````Soar +```Soar +sp {mac*propose*operator*move-mac-boat2 + (state ^name mac + ^ << right-bank left-bank >> ) + ( ^{ << cannibals missionaries >> } > 1 + ^boat 1) +--> +( ^operator + =) +( ^name move-mac-boat +^bank +^ 2 +^boat 1 +^types 1)} +```` + +Now try to write the third proposal for moving one missionary and one +cannibal. + +```Soar +sp {mac*propose*operator*move-mac-boat11 + (state ^name mac + ^ << right-bank left-bank >> ) + ( ^missionaries > 0 + ^cannibals > 0 + ^boat 1) +--> + ( ^operator + =) + ( ^name move-mac-boat + ^bank + ^missionaries 1 + ^cannibals 1 + ^boat 1 + ^types 2)} +``` + +## Operator Application + +The operator application rules must change the state to reflect the +movement of the boat and the missionaries and cannibals that cross the +river. As part of applying the operators, it is not necessary to +represent that the missionaries and cannibals are in the boat, only that +they change banks of the river. The changes that need to be made to the +state are to decrease the number of missionaries and cannibals that are +moving from the bank of the river that the boat is on, and increase the +number on the bank the boat is moving to. Similarly, the count of the +boat (0 or 1) must be changed. You might try to come up with a set of +rules to do this, but because of the operator representation, a single +rule can make changes for moving cannibals, missionaries and the boat +from either bank of the river to the other. The rule must test for an +augmentation of the operator, such as `^boat`, `^cannibals`, or +`^missionaries`, and then change the corresponding subobject on the state. +The rule will fire in parallel for all entities being moved, including +the boat. + +Below is an English version of the required rule. + +```Soar +# mac*apply*move-mac-boat +# If there is a move-mac-boat operator selected for a type and number, then +# subtract the values of that type on the current bank and add those values +# to the other bank. +``` + +Converting this to a Soar rule is a bit tricky because of all of the +variables. To simplify the conversion, we will start with a rule that +applies the operator for moving one cannibal. Try to write this rule +yourself. + +```Soar +# mac*apply*move-mac-boat*one*cannibal +# If there is a move-mac-boat operator selected for one cannibal, then +# subtract one from cannibal object on the left bank and add one to the +# cannibal object on the other bank. +``` + +```Soar +sp {apply*move-mac-boat*one*cannibal + (state ^operator ) + ( ^name move-mac-boat + ^cannibals 1 + ^bank ) + ( ^cannibals + ^other-bank ) + ( ^cannibals ) +--> + ( ^cannibals - + (- 1)) + ( ^cannibals - + (+ 1))} +``` + +The above rule tests the operator to ensure that one cannibal is being +moved `( ^cannibals 1)` and to detect the bank of the operator. It +then matches the number of cannibals on that bank, matching +``, via the other-bank attribute, matching ``. The +actions of the rule modify the number of cannibals on the left bank by +rejecting the current value `(^cannibals -)`, and by +asserting the new value which is the original value minus one +`(^cannibals (- 1))`. Arithmetic operations such as addition, +subtraction, and multiplication are done in Soar using prefix notation +where the operation is given first followed by the operands. + +One concern you might have about the above rule is that it will apply +multiple times if there is more than one cannibal on the left bank of +the river, moving each cannibal, one by one to the other bank. However, +that will not happen because immediately after this rule fires (multiple +times in parallel for each entity being moved) the rule that proposed +the operator will no longer match, causing the operator to be removed. +The operator proposal rule will no longer match because it tested the +number of cannibals on the left bank of the river, which is changed by +the rule. In addition, the boat will move from one bank to another at +the same time, providing a second reason for the proposal rule not to +match. Thus, the operator will terminate immediately after the above +rule fires. + +The next step is to generalize this rule so it can apply to moving 1 or +2 cannibals. This requires replacing the test for ^cannibals 1 on the +operator to `^cannibals ` and then using `` in the +actions to subtract from the current + +```Soar +sp {apply*move-mac-boat*cannibal + (state ^operator ) + ( ^name move-mac-boat + ^cannibals + ^bank ) + ( ^cannibals + ^other-bank ) + ( ^cannibals ) +--> + ( ^cannibals - + (- )) + ( ^cannibals - + (+ ))} +``` + +The final generalization is to replace the test for the `^cannibals` +attribute of the operator with a more general test that matches +cannibals, missionaries, or the boat to a variable ``. That +variable is used to match the appropriate object on the state. This rule +will now fire multiple times to move the boat as well as any cannibals +or missionaries that are moving. + +```Soar +sp {apply*move-mac-boat + (state ^operator ) + ( ^name move-mac-boat + ^{ << missionaries cannibals boat >> } + ^bank ) + ( ^ + ^other-bank ) + ( ^ ) +--> + ( ^ - + (- )) + ( ^ - + (+ ))} +``` + +## State and Operator Monitoring + +Below are three rules that monitor the selected operator and the state +(one rule for each bank that the boat is on). + +```Soar +sp {monitor*move-mac-boat + (state ^operator ) + ( ^name move-mac-boat + ^{ << cannibals missionaries >> } ) +--> + (write | Move | | | )} +``` + +```Soar +sp {monitor*state*left + (state ^name mac + ^left-bank + ^right-bank ) + ( ^missionaries + ^cannibals + ^boat 1) + ( ^missionaries + ^cannibals + ^boat 0) +--> + (write (crlf) | M: | |, C: | | B ~~~ | + | M: | |, C: | | |)} +``` + +```Soar + sp {monitor*state*right + (state ^name mac + ^left-bank + ^right-bank ) + ( ^missionaries + ^cannibals + ^boat 0) + ( ^missionaries + ^cannibals + ^boat 1) +--> + (write (crlf) | M: | |, C: | | ~~~ B | + | M: | |, C: | | |)} +``` + +When you run your program, you will observe that your program runs +forever and also sometimes visits states that are illegal according to +the problem statement. + +## Desired State Recognition + +The next step in creating a program to solve missionaries and cannibals +is creating a rule that recognizes when a desired state has been +achieved. Although a rule specific to the given problem can easily be +written, it might be better to write one that is more general. For +example, you might assume that the desired state will always have some +number of missionaries, cannibals, on one bank of the river. + +The action of the rule should be to print out a message that the problem +has been solved and halt. + +Write an English version of this rule. + +````Soar +# mac*detect*goal*achieved +# If the name of the state is mac and the number of missionaries and +# cannibals on one bank of the river in the desired state matches the number +# of missionaries and cannibals on the same bank in the current state, write +# that the problem has been solved and halt. +```Soar + +Translating this into Soar is relatively straightforward. Try to write +your own before looking below. + +```Soar +sp {mac*detect*state*success + (state ^desired + ^ ) + ( ^missionaries + ^cannibals ) + ( ^{ << right-bank left-bank >> } ) + ( ^missionaries + ^cannibals ) +--> + (write (crlf) |The problem has been solved.|) + (halt)} +```` + +If you run this with the earlier rules, the program should halt at some +point; however, it is likely that it will visit a failure state and thus +solved the problem incorrectly. + +## State Failure Detection + +The next step is creating a rule that recognizes when a failure state +has been encountered. According to the problem statement, a failure +state is one where the cannibals out number the missionaries on one bank +of the river. One condition that is often forgotten is to test that the +number of missionaries is greater than zero. The action for this rule is +to print out a message that the problem has failed to be solved, and +then halt. Write an English version of this rule. + +```Soar +# mac*detect*goal*failure +# If the name of the state is mac and there are more cannibals than +# missionaries, and there is at least one missionary, on one bank of the +# river, then write that the problem has failed to be solved, and halt. +``` + +Translating this into Soar is relatively straightforward. Try to write +your own before looking below. + +```Soar +sp {mac*evaluate*state*failure*more*cannibals + (state ^desired + ^<< right-bank left-bank >> ) + ( ^missionaries { > 0 } + ^cannibals > ) +--> + (write (crlf) |The problem has failed.|) + (halt)} +``` + +Try running your complete program. Invariably the program will halt with +failure because of the high likelihood of encountering a failure state. + +## Search Control: Undoing the Last Operator + +In the current problem, when a failure is reached, the program halts. +One possibility is to have the program start over again from the initial +state. But if you were working on the problem, you would probably notice +that you reached an illegal state, and you would go back one step by +undoing the last operator and try to find another path. In order to undo +the last operator, you must remember what it was. You can use some of +the work you did on the Water Jug where you created a memory to _avoid_ +undoing the last operator to _prefer_ to undo an operator when a failure +state is achieved. + +Given the representation of the move-mac-boat operator in working +memory, you will have to write two rules to record the last operator, +one that handles instances of the operator that move a single type of +entity, and a second that handles instances of the operator that move +one missionary and one cannibal. The action of these rules should create +an augmentation of the state with information on the operator that is +being applied. Try to write English versions of these rules. + +```Soar +mac*apply*move-mac-boat*record*last-operator*types*1 +``` + +If an operator is selected to move one type of entity, then create an +augmentation of the state (last-operator) with the bank of the boat, the +type of entity being moved, the number, and that there is one type being +moved. + +```Soar +mac*apply*move-mac-boat*record*last-operator*types*2 +``` + +If an operator is selected to move two types of entity, then create an +augmentation of the state (last-operator) with the bank of the boat and +that there is two types being moved. + +These can then be converted into Soar rules: + +```Soar +sp {mac*apply*move-mac-boat*record*last-operator*types*1 + (state ^name mac + ^operator ) + ( ^name move-mac-boat + ^bank + ^{ << missionaries cannibals >> } + ^types 1) +--> + ( ^last-operator ) + ( ^types 1 + ^bank + ^type + ^number )} +``` + +```Soar +sp {mac*apply*move-mac-boat*record*last-operator*types*2 + (state ^name mac + ^operator ) + ( ^name move-mac-boat + ^bank + ^types 2) +--> + ( ^last-operator ) + ( ^types 2 + ^bank )} +``` + +The rule to remove old records only has to test if the bank of the boat +in the record of the last operator does not match the current bank that +the boat is on because each time an operator is applied the boat changes +banks. + +```Soar +mac*apply*move-mac-boat*remove*old*last-operator +``` + +If the move-mac-boat operator is selected and the bank in the +last-operator is not equal to the bank of the current boat, remove the +last-operator structure. + +This can then be converted into Soar a rule: + +```Soar +sp {mac*apply*move-mac-boat*remove*old*last-operator + (state ^name mac + ^operator + ^.other-bank + ^last-operator ) + ( ^bank ) +--> + ( ^last-operator -)} +``` + +Once you add these rules, you can now add rules that undo an operator +whenever one leads to a failure state. However, you must first modify +the rule that detects failure so that it doesn’t halt the program, but +just augments the state with failure: + +```Soar +# mac*detect*goal*failure +# If the name of the state is mac and there are more cannibals than +# missionaries, and there is at least one missionary, on one bank of the +# river, then augment the state with failure true. +``` + +Translating this into Soar is relatively straightforward. + +```Soar +sp {mac*evaluate*state*failure*more*cannibals + (state ^desired + ^<< right-bank left-bank >> ) + ( ^missionaries { > 0 } + ^cannibals > ) +--> + ( ^failure )} +``` + +Note that this rule only fires when there is an illegal state and it is +not part of the application of an operator. Thus, it will retract and +remove the failure augmentation automatically if the state changes and +there is no longer an illegal state. + +Now you can write rules that prefer operators that undo the last +operator when there is failure. Just as before, this will require two +rules, one for moving a single type of entity, and one that moves one +missionary and one cannibal. Below is a general English version for both +rules. + +```Soar +# mac*select*operator*prefer*inverse*failure +# If the name of the state is mac and the current state is a failure state +# and the last operator is the inverse of a proposed operator, then prefer +# that operator. +``` + +```Soar +sp {mac*select*operator*prefer*inverse*failure*types*2 + (state ^name mac + ^operator + + ^failure + ^last-operator ) + ( ^name move-mac-boat + ^ + ^types 1) + ( ^types 1 + ^type + ^number ) +--> + ( ^operator >)} +``` + +```Soar +sp {mac*select*operator*prefer*inverse*failure*types*1 + (state ^name mac + ^operator + + ^failure true + ^last-operator.types 2) + ( ^types 2) +--> + ( ^operator >)} +``` + +After you have added these rules, your program will be able to solve the +problem; however it will probably take a very indirect path to the +solution. One reason is that after an operator has been successfully +applied and generated a valid state, the inverse of that operator will +often be selected, undoing the operator and wasting both operator +applications. To avoid this, you can add two more rules that _avoid_ +undoing the last operator when the state is not a failure state. + +```Soar +# mac*select*operator*avoid*inverse*not*failure +# If the name of the state is mac and the current state is not a failure +# state and the last operator is the inverse of a proposed operator, then +# avoid that operator. +``` + +```Soar +sp {mac*select*operator*avoid*inverse*not*failure*1 + (state ^name mac + ^operator + + -^failure true + ^last-operator ) + ( ^types 1 + ^ ) + ( ^types 1 + ^type + ^number ) +--> + ( ^operator < )} +``` + +```Soar +sp {mac*select*operator*avoid*inverse*not*failure*2 + (state ^name mac + ^operator + + -^failure true + ^last-operator ) + ( ^types 2) + ( ^types 2) +--> + ( ^operator < )} +``` + +After you have added these final rules, your program should solve the +problem much quicker. However, you will notice that there is still some +inefficiency. For example, at the initial state, it is possible for the +program to attempt to apply the same operator to the state after it has +failed with moving one or both the other missionaries. The figure below +shows the initial state and the three successive states that it can +cycle among. The problem is that only the most recent operator for a +current state is recorded. It is not possible to associate all prior +operators that applied to a state because the state is continually +changing. + +![](Images/04/4.png) + +In the next Part of the tutorial, you will learn how to use +impasses and substates so that your programs can use look-ahead planning +and solve this type of problem more directly. diff --git a/docs/tutorials/soar_tutorial/05.md b/docs/tutorials/soar_tutorial/05.md new file mode 100644 index 00000000..7329dfe5 --- /dev/null +++ b/docs/tutorials/soar_tutorial/05.md @@ -0,0 +1,982 @@ +# Part V: Planning and Learning + +This part of the tutorial will teach you to build Soar programs that use +subgoals to plan and learn. The type of planning that you will learn +about in this section is simple look-ahead planning. Other types of +planning are possible in Soar, but this is the most natural. In fact, +you will need to make only minor changes and a few additions to use +planning in the Water Jug and Missionaries and Cannibals problems. This +type of learning is called chunking, which is a form of +explanation-based generalization. Within the examples in this section, +chunking speeds up problem solving by creating search-control rules. In +other types of problem solving, chunking creates other types of rules +including elaborations, operator proposals and operator applications. + +In simple look-ahead planning, your program will try out operators internally to +evaluate them before it commits to applying them in the actual task. In the +programs that you developed in [Part I](01.md) and +[Part IV](04.md), all of the problem solving was +internal, so it didn’t really matter if a mistake was made. For example, in the +Missionaries and Cannibals problem, you added rules to backup when a failure +state was reached. However, in problems that involve interaction with a world +where you can’t always undo an action, it is often critical to try out actions +internally before executing them in the world. In addition, planning provides a +way of comparing alternative operators based on the states they produce. +Chunking builds rules that summarize the comparisons and evaluations that occur +in the look-ahead search so that in the future the rules fire, making look-ahead +search unnecessary – converting deliberation into reaction. + +Many planning systems have a two-stage cycle of planning and execution. +They always plan when given a new problem and then execute the plan step +by step. Our approach in Soar is different. In Soar, each decision is +made with whatever knowledge is available and planning is one more +source of knowledge that can be used whenever the knowledge encoded +directly as rules is insufficient to make a decision. Thus, planning is +used on an as-needed basis. As we will see later, planning in Soar does +not create a step-by-step sequence of operators to apply blindly, but +instead learns a set of context-dependent rules that prefer or avoid +specific operators for states in the space. As a result, Soar’s plans +are more flexible so that plans learned for one problem may transfer to +part of a second problem. It also makes it so that Soar only plans when +it needs to. + +Our study of planning will parallel the study of problem solving. You +will first modify and extend the rules you wrote for the Water Jug +problem and then the Missionaries and Cannibals problem. + +## The Water Jug Problem + +Planning in Soar arises out of its impasse detection and substate creation +mechanism. Remember in TankSoar how subgoals were created when an operator was +selected but there were no rules that could directly apply the operator. Soar +has other impasses that arise when progress cannot be made for other reasons +(see the manual for a complete description of [impasses and +substates](../../soar_manual/02_TheSoarArchitecture.md#impasses-and-substates)). +Soar automatically creates a subgoal whenever the preferences are insufficient +for the decision procedure to select an operator. This is a signal that there is +insufficient knowledge to make a decision and further problem solving is +required. The decision procedure can choose an operator if there is either one +clear winner (one operator that dominates the others based on the available +preferences), or when there are multiple operators that are all indifferent. In +the rules you’ve written so far, there have always been indifferent preferences +for every operator, so that the decision procedure always selected randomly +among the preferred operators. However, indifferent preference should be used +only when it is known that one operator is no better than another, instead of +when it is not known which operator is best. Therefore, the first step to allow +planning to be invoked is to eliminate the indifferent preference from the +operator proposal rules. + +Below is the operator proposal for the fill operator in the Water Jug +without the indifferent preference. + +```Soar +sp {water-jug*propose*fill + (state ^name water-jug + ^jug ) + ( ^empty > 0) +--> + ( ^operator +) + ( ^name fill + ^jug )} +``` + +Once that indifferent preference is removed, rerun your program and step +through the first decision. You will discover that Soar automatically +generates a new substate in situations where the operator preferences +are insufficient to pick a single operator. This is called a _tie +impasse._ + +When a tie impasse arises, a new substate is created that is very +similar to the substate created in response to an operator no-change. +Print out the substate and examine the augmentations. The key +differences are that an operator tie has `^choices multiple `, `^impasse +tie`, and `^item` augmentations for each of the tied operators. + +For the operator no-change impasses you used in TankSoar, the goal was +to apply the selected operator to its state. This was achieved by +selecting and applying operators in the substate that modify the +superstate either directly (by adding or removing augmentations) or +indirectly (by performing actions in the world that led to changes in +the sensors that are part of the state). For a tie impasse, the goal is +determine which task operator is the best to apply to the current state. +This will be achieved by selecting and applying _evaluation_ operators +in the substate, one for each of the task operators. The purpose of +these operators is to create an evaluation of the task operators, such +as failure, success or a numeric evaluation of the likelihood of +success. These evaluations will then be converted in preferences for the +task operators. Once sufficient preferences have been created to select +a task operator, it will be selected and the substate will automatically +be removed from working memory. It is possible that after evaluating +only a subset of the tied task operators, sufficient evaluations will be +created so that preferences are generated that break the tie. For +example, if an operator is evaluated and it is determined that it leads +directly to the goal, a best preference can be created. This might be +sufficient to break the tie, select an operator, and resolve the +impasse. + +One way in which task operators can be evaluated is by trying them out +on internal copies of the states in which the tie occurred, and then +evaluating the states they create. The evaluation of states can be based +on whether they are at desired state, a failure state (as in +Missionaries in Cannibals), or the initial state. Many problems also +lend themselves to an evaluation function that can be applied to a state +that estimates how close the state is to the goal. For example, in +Chess, numbers are assigned to the different pieces and you compare the +value of your pieces to your opponents. + +We will call the problem of resolving a tie impasse, the _Selection_ +problem throughout the rest of the tutorial. We will treat the +development of rules and operators for the selection problem just as we +would treat the development of a program to solve other problems, such +as the Water Jug or Missionaries and Cannibals, and go through the same +steps we went through for those problems. Although coming up with good +state evaluation functions for a specific problem can be challenging, +the overall approach is very general and requires only a few bits of +domain-specific information. In fact, we have created a set of generic +Soar operators/rules that carry out the evaluation and comparison of +operators using this approach. This set of generic rules can be used in +a wide variety of problems for simple planning and they are available as +part of the Soar release. These rules are part of the default rules that +come with the Soar release in the file Agents/default/selection.soar. +You should load these rules when you load in the Water-jug rules. You +can do this by adding the following commands to your files (assuming +that your program is in a subdirectory of the Agents directory): + +```Soar +pushd ../default +source selection.soar +popd +``` + +By going through the rest of this section you will learn how these rules +work and what you need to provide for them to be used on other problems. + +From our previous work on solving problems in Soar, here are the types +of knowledge we need to consider for the selection problem: + +1. The state representation. This includes the representation of + evaluation objects that link evaluations with operators. +1. The initial state creation rule. Since this problem arises as a + substate, an initial state is generated automatically. +1. The operator proposal rules. The only operator is evaluate-operator + and it must be proposed for every tied operator. +1. The operator application rules. Evaluate-operator is implemented + hierarchically, like the abstract operators in TankSoar. Therefore, + operator application involves a substate and substate operators. +1. The operator and state monitoring rules. There are no special + monitoring rules for Selection. +1. The desired state recognition rule. Desired state recognition is + automatic. When there are sufficient preferences, the decision + procedure selects an operator, the impasse is resolved, and the + substate is removed from working memory. +1. The failure recognition rule. There are no failure states for this + problem. +1. The search control rules. These rules help guide which operator + should be evaluated first. + +### Selection State Representation + +For the selection problem, the operators are going to create and compare +evaluations. Eventually, preferences for the superstate will be created, +but those do not have to be represented in the Selection problem. + +If the evaluations had only a single value, namely the evaluation, then +they could be represented as simple augmentations of the state, such as: +`^evaluation` success. However, the evaluations must also include the task +operator that the evaluation refers to. Moreover, we will find it useful +to have different types of evaluations that can be compared in different +ways, such as symbolic evaluations (success, failure) or numeric +evaluations (numbers over some ordered range). We will also find it +useful to create the evaluation structure before the evaluation is +known, so that it will be useful to have an augmentation of the +evaluation object that signifies that a value has been created. +Therefore, the state should consist of a set of evaluation +augmentations, with each evaluation object having the following +structure: + +- operator `` the identifier of the task operator being evaluated +- symbolic-value success/partial-success/partial-failure/failure/indifferent +- numeric-value `[number]` +- value true indicates that there is either a symbolic or numeric value. +- desired `` the identifier of a desired state to use for evaluation if there is one + +Another alternative to creating evaluations on the state is to create +evaluations on the operators themselves. The advantage of creating +evaluations on the state is that they are automatically removed when the +impasse is resolved. + +### Selection Initial State Creation + +In the previous problems in this part, you have had to create a rule +that generates the initial state. However, for the selection problem, +the state is automatically created in response to an impasse. +Furthermore, the evaluation objects will be created by operator +applications. The one initial state elaboration rule that is necessary +is one that names the state selection. This rule really isn’t necessary, +but will allow us to test for the name of the state instead of the +impasse type in all of the remaining rules. + +```Soar +sp {default\*selection*elaborate*name + +:default +(state ^type tie) +--> +( ^name selection)} +``` + +This rule uses a new bit of syntax, the :default, which tells Soar that +this is a default rule. Default rules do not behave any differently than +other rules; however, Soar keeps separate statistics on default rules +and allows you to remove all of the non-default rules easily. This rule +and all others labeled default are included in the selection.soar file. + +### Selection Operator Proposal + +The only operator in the Selection problem is evaluate-operator. This +should be proposed in the Selection problem if there is an item that +does not have an evaluation with a value. The operator will first create +an evaluation and later compute the value. + +```Soar +Selection*propose*evaluate-operator +``` + +If the state is named selection and there is an item that does not have +an evaluation with a value, then propose the evaluate-operator for that +item. + +The tricky part of translating this into a rule is the test that there +is an item without an evaluation with a value. The item can be matched +on the state as: `^item `. The test that no evaluation exists is +going to be a _negation_ of an evaluation object `( ^evaluation )`. +The evaluation object must also include a reference to the item, +which is encoded as `( ^operator )`. An additional test +`( ^value true)` must be included in the negation so that the rule will +match only if there does not exist an evaluate with a value, giving: + +```Soar +sp {selection*propose*evaluate-operator + :default + (state ^name selection + ^item ) + -{(state ^evaluation ) + ( ^operator + ^value true)} +--> + ( ^operator +, =) + ( ^name evaluate-operator + ^operator )} +``` + +Given these conditions, once an evaluate-operator operator is selected, +it will stay selected until an appropriate evaluation is created with a +value. Therefore, one and only one evaluate-operator operator will be +created and applied for each of the tied task operators. + +Unlike the Water Jug and Missionaries and Cannibals operators, these +operators can be made indifferent. It is unlikely that it would be +useful to do problem solving to decide which operator should be +evaluated first. However, there might be some cases where some knowledge +about the task can be used to order the evaluation of operators. For +example, many Chess playing programs use iterative deepening to order +the evaluation. Moreover, in a later section, we will see an example +where it is useful to add some search control knowledge for +evaluate-operator selection. + +### Selection Operator Application + +The application of the evaluate-operator operators has two parts. The +first part is the creation of the evaluation data structure, but without +any value. In parallel with the creation of the evaluation structure, +the operator is elaborated with additional structures to make +application simpler. The end result is the following: + +```Soar +( ^evaluation + ^operator ) +( ^superoperator + ^desired ) +( ^name evaluate-operator + ^superoperator + ^evaluation + ^superstate + ^superproblem-space ) +``` + +The desired augmentation is an object that describes the desired state +for the original task. In Water Jug, it was an object that described the +three-gallon jug having a single gallon of water. In the Missionaries +and Cannibals, it was an object that described having all of the +missionaries and cannibals on the right side of the river. The desired +structure is included so that the evaluation of an operator can be based +on how well it helps achieve the desired state. This might be by +achieving the desired state, or it might be based on a heuristic +calculation of distance from the desired state to the state created by +applying the operator being evaluated. + +The superproblem-space augmentation is the problem-space object of the +superstate. What is a problem-space object? It is an explicit +representation on the state of properties of the current problem space +and can be used to cue the firing of rules relevant to the current +problem space. The name augmentation of the state has served this +purpose in the previous systems; however, it is insufficient in general +because it contains only a single symbol, the name. By having an object, +instead of just a name, additional properties of the problem space can +be included. This is important in planning because the look-ahead search +must use the same problem space and must copy the current state, which +requires information about the structure of the problem space states.. + +The second part is the calculation of the evaluation, which cannot be +done directly with rules but requires its own substate. This substate is +called to the _evaluation_ problem. The original task state is copied to +the evaluation substate. Then, the task operator being evaluated is +applied to that state. If that new state can be evaluated, then the +evaluation is returned as a result and added to the appropriate +evaluation object. If no evaluation is possible, then problem solving in +the _task_ (such as the Water Jug) continues until an evaluation can be +made. That may lead to more tie impasses, more substates + + + +The key computations that have to be performed are: + +1. Creating the initial state +1. Selecting the operator being evaluated +1. Applying the selected operator +1. Evaluating the result + +### Creating the Initial State + +The initial state must be a copy of the state in which the tie impasse +arose. Copying down all of the appropriate augmentations can be done by +a few Water Jug specific rules; however, that means that with each new +task, new state copy rules must also be written. To avoid this, we have +written a general set of rules that can copy down the augmentations. +These rules match against augmentations of the problem space to +determine with state augmentations to copy. Below are the legal +augmentations dealing with state copying and their meaning: + +- default-state-copy no: Do not copy any augmentations automatically. +- one-level-attributes: copies augmentations of the state and preserves their value. + +Example: + +```Soar +(p1 ^one-level-attributes color) (s1 ^color c1) (c1 ^hue green) -> +(s2 ^color c1) +``` + +- two-level-attributes: copies augmentations of the state and creates + new identifiers for values. Shared identifiers replaced with same + new identifier. + +Example: + +```Soar +(p1 ^two-level-attributes color) (s1 ^color c1) (c1 ^hue green) -> +(s2 ^color c5) (c5 ^hue green) +``` + +- all-attributes-at-level one: copies all attributes of state as + one-level-attributes (except dont-copy ones and Soar created ones + such as impasse, operator, superstate) + +Example: + +```Soar +(p1 ^all-attributes-at-level one) (s1 ^color c1) (s1 ^size big) -> +(s2 ^color c1) (s2 ^size big) +``` + +- all-attributes-at-level two: copies all attributes of state as + two-level-attributes (except dont-copy ones and Soar created ones + such as impasse, operator, superstate) + +Example: + +```Soar +(p1 ^all-attributes-at-level two) (s1 ^color c1) (c1 ^hue green) -> +(s2 ^color c5) (c5 ^hue green) +``` + +- dont-copy: will not copy that attribute. + +Example: + +```Soar +(p1 ^dont-copy size) +``` + +- don’t-copy-anything: will not copy any attributes + +```Soar +(p1 ^dont-copy-anything yes) +``` + +If no augmentations relative to copying are included, the default is to +do all-attributes-at-level one. The desired state is also copied over, +based on the copy commands for the state. + +These rules support two levels of copying. How should you decide what +level of augmentations need to be copied? In the water jug, the state +has two levels of structure: the jugs and their contents, volume, and +empty: + +```Soar +(s1 ^jug j1 j2) +(j1 ^volume 3 + ^contents 0 + ^empty 3) +(j2 ^volume 5 + ^contents 0 + ^empty 5) +``` + +Is it sufficient to copy the augmentations of the state, or do the +subobjects need to be copied as well? To answer this question, you need +to look closely at how the operators modify the state. In the +formulation of the problem we’ve been using, the operators change the +augmentations of the jugs. For example, the application rule for fill +is: + +```Soar +sp {water-jug*apply*fill + (state ^operator + ^jug ) + ( ^name fill + ^jug ) + ( ^volume + ^contents 0 + ^empty ) +--> + ( ^contents + 0 - + ^empty 0 + - )} +``` + +This rule modifies the contents and empty augmentations. So if this is +used to apply the fill operator to the three-gallon jug using the +working memory elements above, `(j1 ^contents 0)` and `(j1 ^empty 3)` will +be removed from working memory and `(j1 ^contents 3)` and `(j1 ^empty 0)` +will be added to working memory. The result will be that not only is the +“copy” modified, but also the original state. Thus, this is unacceptable +because it does not make it possible to apply operators to substate +without modifying the original. + +There are two possible solutions. The first is to copy two levels of +attributes. This can be achieved by adding the following to the actions +of original state initialization rule for Water Jug: + +```Soar +sp {water-jug*elaborate*problem-space + (state ^name water-jug) +--> + ( ^problem-space

) + (

^name water-jug + ^default-state-copy yes + ^two-level-attributes jug)} +``` + +You could use ^all-attributes-at-level two instead, but it is best to +list exactly the attributes you need to have copied. After using this, +the substate, s3, would have the following structure: + +```Soar +(s3 ^jug j3 j4) +(j3 ^volume 3 + ^contents 0 + ^empty 3) +(j4 ^volume 5 + ^contents 0 + ^empty 5) +``` + +The second approach is to change the operator applications so that they +do not modify the augmentations of the jug objects but instead create +completely new jug objects. For example, a modified version of fill +would be: + +```Soar +sp {water-jug*apply*fill + (state ^operator + ^jug ) + ( ^name fill + ^jug ) + ( ^volume + ^contents 0 + ^empty ) +--> + ( ^jug - + ^jug ) + ( ^volume + ^contents + ^empty 0)} +``` + +The disadvantage of this approach is that it requires more changes to +working memory when an operator is applied, although it requires fewer +working memory elements to be copied during the creation of the initial +state. This approach also is less natural in that it implies that a new +jug is created as opposed to just modifying the contents of the existing +jug. For these two reasons, the first approach (two-level-attribute +copying) is preferred. + +### Selecting the operator being evaluated + +Once the initial state of the evaluate-operator operator is created, a +copy of the operator being evaluated needs to be selected. The reason a +copy is necessary is two fold. First, the original operator may have +augmentations that refer to objects in the original (non-copied) state. +Therefore, a copy of the operator must be made with those new objects. +In the Water Jug, the operator for fill has an augmentation for the jug +being filled: ( ^name fill ^jug j1). The second reason is that in +course of applying some operators, augmentations are added to the +operator, which in this case would modify the original operator before +it is applied. For these reasons, a duplicate of the operator is +automatically made (unless ^default-operator-copy no is an augmentation +on the problem space). The copying replaces any identifiers in the +operator structure that were changed in the state copying process (in +this case j1 would be replaced by j3). + +Once a copy is made, additional rules reject all of the other proposed +operators so that the copy will be selected. + +### Applying the selected operator + +Once the operator is selected, the rules for applying it will fire and +modify the copied state. You do not need to add any addition rules or +modify existing rules. For tasks where the original operators involve +action in an external world, such as in Eaters or TankSoar, you would +have to write rules to simulate the effects of the selected operator on +the state. For example, in Eaters, rules would have to be written to +simulate the movement of the eater to a new square. The simulation can +be only approximate because the eater does not have access to the +complete world map so it cannot update sensors for squares it has not +seen. + +### Evaluating the result + +Once the new state is created, an evaluation can be made. An +augmentation is added to the state in parallel to the operator being +applied: ^tried-tied-operator . This augmentation can be tested by +rules to ensure that they are evaluating the result of applying the +operator as opposed to the copy of the original state – although this +will not work for operators that apply as a sequence of rules. + +The simplest evaluations to compute are success and failure. Success is +when the goal is achieved. You have already written a rule that detects +success for the Water Jug; however, this just halts the problem solving. +This rule can be modified to create a working memory element that is +used as an evaluation. The selection rules will automatically process an +evaluation on a state in an evaluation state. The evaluation has two +parts. The attribute is the type of evaluation and the value must be the +identifier of the desired state. This ensures that the evaluation is +done relative to the correct desired state. Therefore, the rule for +detecting success should be modified to be as follows: + +```Soar +sp {water-jug*evaluate*state*success +(state ^desired +^problem-space.name water-jug +^jug ) +( ^jug ) +( ^volume ^contents ) +( ^volume ^contents ) +--> +( ^success )} +``` + +In addition to success and failure, the selection rules can process +other symbolic evaluations as well as numeric evaluations. The symbolic +preferences that are processed are as follows: + +- success: This state is the desired state. This is translated into a + best preference. It is also translated into a better preference if + another operator has a result state with an evaluation of + partial-success. + +- partial-success: This state is on the path to success. This is + translated into a best preference. + +- indifferent: This state is known to be neither success of failure. + This is translated into an indifference preference. + +- failure: The desired state cannot be achieved from this state. This + is translated into a reject preference. + +- partial-failure: All paths from this state lead to failure. This is + translated into a worst preference. + +For numeric evaluations, an augmentation named ^numeric-value should be +created for the evaluation object for an operator. We will discuss +numeric evaluations in more detail in a future section. + +If you include your original rules, the selection rules, and the two new +rules described above (water-jug*elaborate*problem-space, +water-jug*evaluate*state\*success), your system will start doing a +look-ahead search. You will notice that at each operator selection there +is a tie and a recursive stack of substates is created. If the solution +is ever found, it is only used locally to select the operator for the +most recent tie impasse. As a result, it will have to rediscover the +solution as it slowly “backs out” of the substates. Another problem is +that the system will often revisit the same state and generate an +identical tie impasse deeper in the stack of states. The resolution of +the first problem will wait until we introduce chunking. + +You can resolve the second problem by evaluating states that are already +in the stack as failure. That means that if an operator is being +evaluated, and if it produces a state that is the duplicate of a +previous state (one already on the state stack), then it is considered a +failure. Not only will this avoid long loops but it will also mean that +the operator that undoes the most recent operator will be avoided. The +good news is that by using this we can eliminate the rules for +remembering the last operator. + +What does this rule need to have as its conditions? It needs to test the +following: + +1. The desired state, which is used in the action: `( ^desired )` +1. The contents of the state. +1. That state exists _after_ the operator has been applied in an + evaluation. Remember from earlier in this section that the correct + test for this is: `( ^tried-tied-operator)` +1. That there is a duplicate of that state that is earlier in the state + stack. There is nothing inherent in Soar that keeps track of all + earlier states – only the superstate is readily available. We need + to add rules that elaborate each state with all of its superstates + (and their superstates). We will call this the superstate-set. + Computing this requires only two rules – one that adds its + superstate to the superstate-set and one that adds all of the + superstate-set of the superstate to its superstate-set. + +```Soar +sp {Impasse__Operator_Tie*elaborate*superstate-set +(state ^superstate ) +--> +( ^superstate-set )} +``` + +```Soar +sp {Impasse__Operator_Tie*elaborate*superstate-set2 +(state ^superstate.superstate-set ) +--> +( ^superstate-set )} +``` + +The rule to detect failure is then as follows: + +```Soar +sp {water-jug*evaluate*state*failure*duplicate +(state ^name water-jug +^superstate-set +^jug +^jug +^tried-tied-operator) +( ^volume 5 ^contents ) +( ^volume 3 ^contents ) +( ^name water-jug +^desired +^jug +^jug ) +( ^volume 5 ^contents ) +( ^volume 3 ^contents ) +--> +( ^failure )} +``` + +With this rule added, the problem solving will be more directed, but it +will take an excessive number of decisions to solve the problem. The +problem is that as soon as a result is produced, such as that in a given +state a specific operator leads to failure, it is forgotten so that the +next time that state is encountered, the evaluation must be repeated. +What is needed is some memory that relates a state and operator with a +result, which is essentially a rule. Conceptually, the conditions that +should be included in the rule are templates of the working memory +elements that were tested in the processing that produced the result. +This is exactly what chunking does. + +### Chunking + +Chunking is invoked when a result is produced in a substate. The result +will be the action of a new rule. Chunking examines the working memory +elements that were matched by the rule that created the result. If any +of those working memory elements are linked to superstates, they become +conditions in the new rule. For each of the remaining working memory +elements, chunking finds the rule and associated working memory elements +that were responsible for generating it and recursively _backtraces_ +until all of the conditions are collected. Some of the working memory +elements tested in conditions require special processing. For example, +if one of the tied operators is tested via an item augmentation of a +state is tested in a rule that leads to the inclusion of a test for an +acceptable preference for that operator in the superstate. + +This process requires that Soar maintain a copy of every instantiated +rule firing in a substate. Once all of the conditions have been +collected, Soar converts all identifiers that were in the working memory +elements into variables and adds the rule to rule memory. The rule is +immediately available for matching. + +Because chunking is based on the problem solving in a subgoal, the +generality of any rule is determined by what was tested by the rules +that participated in producing the result. If the original rules tested +working memory elements that were not necessary to produce the result, +then the chunk will also test for them and the new rule will be overly +specific. + +To invoke chunking, type in: `learn –-on` at the prompt of the interaction +window or add `learn –-on` as a line in the file containing your rules. +You will also want to see when chunks are created and fire, which is +enabled using: `watch -–chunks –-print`. Before rerunning your program, +try to predict what types of rules will be learned. There should be two +different types of rules because there are two different types of +results: preferences created by comparing evaluations in the selection +problem, and evaluations created in the evaluation substate. Rules for +the first type will test that there is an acceptable preference for the +operator, as well as features of the operator and state that led to the +creation of the preference. Rules of the second type will test that +there is an evaluate-operator operator selected and features of the +operator and state being evaluated. + +Now run and examine the trace. Print out the chunks. + +## The Missionaries and Cannibals Problem + +Now that you have done the conversion of the Water Jug to a simple +planner, it should be relatively straightforward to convert the +missionaries and cannibals. In this section we go through the conversion +quickly, highlighting a problem, and then exploring the use of numeric +evaluations in addition to symbolic evaluations. + +### Conversion to Planning and Learning + +You should try to do the conversion yourself. There is one very subtle +problem that will arise when you start to use learning. When you run +into trouble, try to figure it out, and then read the rest of this +section. Hint: Do not forget to alter your move-mac-boat proposals so +that they no longer have an indifferent preference. + +### Add selection rules + +The first step is to add the following to your program so that the +selection rules are added in: + +```Soar +pushd ../default +source selection.soar +popd +``` + +### Add problem space and state copying information + +The second step is to add the rule that defines the problem space and +specifies how the state copying should be done for evaluation. If you +look back at your implementation of Missionaries and Cannibals, you will +see that the operator application rules modify augmentations of the +left-bank and right-bank structures, so that a two-level attribute copy +is necessary. + +```Soar +sp {mac*elaborate*problem-space + (state ^name mac) +--> + ( ^problem-space

) + (

^name missionaries-and-cannibals + ^default-state-copy yes + ^two-level-attributes right-bank left-bank)} +``` + +### Modify goal detection + +The third step is to modify the goal detection rule so that it creates a +symbolic evaluation of success instead of printing a message and +halting. + +```Soar +sp {mac*detect*state*success +(state ^desired +^ ) +( ^missionaries +^cannibals ) +( ^{ << right-bank left-bank >> } ) +( ^missionaries +^cannibals ) +--> +( ^success )} +``` + +### Modify failure detection + +Although there were no failure states in the Water Jug, there are in the +Missionaries and Cannibals problem. The action of the rule that detects +failure must be modified to create a symbolic value of failure. + +```Soar +sp {mac*evaluate*state*failure*more*cannibals + (state ^desired + ^<< right-bank left-bank >> ) + ( ^missionaries { > 0 } + ^cannibals > ) +--> + ( ^failure )} +``` + +### Add duplicate state detection rule + +The fifth step is to add a rule that detects when there are duplicate +states in the state stack and evaluates the most recent one as a +failure. + +```Soar +sp {mac*evaluate*state*failure*duplicate + (state ^desired + ^right-bank + ^left-bank ) + ( ^missionaries ^cannibals ^boat ) + ( ^missionaries ^cannibals ^boat ) + (state { <> } + ^right-bank + ^left-bank + ^tried-tied-operator) + ( ^missionaries ^cannibals ^boat ) + ( ^missionaries ^cannibals ^boat ) + -(state ^superstate ) +--> + ( ^failure )} +``` + +### Remove last-operator rules + +The last-operator rules are no longer necessary because the planning and +duplicate state detect replace (and improve) the processing they were +designed for. + +After making these six changes, your program should be able to solve the +Missionaries and Cannibals problem with planning. However, just as with +the Water Jugs problem, it may take a very long time. The obvious +solution is to use chunking, Soar’s learning mechanism. The next section +describes a subtle problem that arises when chunking is used. + +### Subtle chunking problem + +When you run your program with chunking enabled, it might stop in the +middle of solving the problem. The reason for this is that with the +current rules for operator implementation, chunking can learn some +control rules that are overgeneral, rejecting an operator that is on the +path to the goal. Specifically, chunking can learn a rule that states: + +If the current state has the boat on the right bank and two missionaries +and two cannibals, reject an operator that moves one missionary and the +boat to the left. + +This rule is learned when the operator to move one missionary to the +left is evaluated in a evaluation subgoal and discovered to lead to a +failure state – where there is one missionary and two cannibals on the +right bank. The problem with this rule is that it doesn’t include a test +that no cannibals are also moved. If the operator moves a cannibal at +the same time it moves a missionary, it does not produce a failure +state. However, with the way the application rule is written, chunking +is unable to pick up any test that no cannibals are being moved. A +satisfactory fix to this problem is to add a test to the operator +application rules for the number of types being modified by the operator +(^types). + +```Soar +sp {mac*apply*move-mac-boat +(state ^operator ) +( ^name move-mac-boat +^{ << missionaries cannibals boat >> } +^bank +^types ) +( ^ +^other-bank ) +( ^ ) +--> +( ^ - +(- )) +( ^ - +(+ ))} +``` + +Once this is included, the learned chunked changes to be: + +If the current state has the boat on the right bank and two missionaries +and two cannibals, reject an operator that moves _only_ one missionary +and the boat to the left. + +Now the system should successfully solve the problem, and after +chunking, solving it in the minimal number of steps. + +### Numeric Evaluations + +Without chunking, Soar takes a very long time to solve the problem +because it does not get any evaluation until the problem is solved, and +then must resolve the problem many times. One approach to avoid the long +search is to add in some evaluations of the states that don’t require +the final state to be achieved. All that is needed is a good evaluation +function. A very simple evaluation function is to prefer states that +have more missionaries and cannibals on the desired bank of the river. +Thus, the evaluation is just the sum of the number of missionaries and +cannibals on the desired bank of the river, with higher numbers +preferred. + +There are selection rules that do all of the domain-independent +processing, such as comparing evaluations and creating the appropriate +preferences. All you need to do is add a rule to compute the evaluation, +and augment the desired state such that higher numbers are better. For +example, the second of these changes leads to the following +initialization rule: + +```Soar +sp {mac*apply*initialize-mac +(state ^operator.name initialize-mac) +--> +( ^right-bank +^left-bank +^desired ) +( ^missionaries 0 +^cannibals 0 +^boat 0 +^other-bank ) +( ^missionaries 3 +^cannibals 3 +^boat 1 +^other-bank ) +( ^right-bank

+^better higher) +(
^missionaries 3 +^cannibals 3 +^boat 1)} +``` + +The rule that computes the evaluation must test the desired state to +determine which bank is the desired one. It must also match the number +of missionaries and cannibals on that bank, as well as test that the +operator being evaluated has applied (^tried-tied-operator). The action +of the operator is to create an augmentation on the state with the +computed evaluation. + +```Soar +sp {mac*evaluate*state*number +(state ^desired +^tried-tied-operator +^ ) +( ^missionaries +^cannibals ) +( ^{ << right-bank left-bank >> } ) +--> +( ^numeric-value (+ ))} +``` + +Now run your system. Without chunking, the solution is found much faster +with this rule. However, once chunking is used, an interesting thing +happens. This rule actually hurts performance. The reason is that the +evaluation is only a heuristic and is sometimes wrong. Chunking captures +both the good and the bad aspects of the evaluation and uses them for +selecting the operators. When this evaluation is not used, all of the +learning is based on absolute features of the task: success and failure. +There are no heuristics that are sometimes wrong, so the rules are +always correct. The lesson is that chunking is only as good as the +problem solving and that sometimes it is not worthwhile to put in partly +correct information. diff --git a/docs/tutorials/soar_tutorial/06.md b/docs/tutorials/soar_tutorial/06.md new file mode 100644 index 00000000..87c6464f --- /dev/null +++ b/docs/tutorials/soar_tutorial/06.md @@ -0,0 +1,529 @@ +# Part VI: Reinforcement Learning + +Let us quickly review the type of operator preferences you have used +thus far in the tutorial. First, acceptability (+): only acceptable +operators are considered for application. Acceptable preferences must be +further described as either relatively differentiated or indifferent. +Differentiated preferences (such as > and <) establish a relative +ordering from which Soar will choose the most preferred operator. If all +preferences are labeled as indifferent (=), Soar’s random operator +choice can be further affected by numerical indifferent parameters. + +Reinforcement learning (RL) in Soar allows agents to alter behavior over +time by dynamically changing numerical indifferent preferences in +procedural memory in response to a reward signal. This learning +mechanism contrasts starkly with chunking. Whereas chunking is a +one-shot form of learning that increases agent execution performance by +summarizing sub-goal results, RL is an incremental form of learning that +probabilistically alters agent behavior. + +### Reinforcement Learning in Action + +Before we get to the nuts and bolts of RL, consider first an example of +its effects. Left-Right is a simple agent that can choose to move either +left or right. Unbeknownst to this agent, one direction is more +preferable than the other. After deciding its destination, the agent +will receive a “reward,” or feedback regarding how good a decision it +made. In this case, it will receive a reward of -1 for moving left and ++1 for moving right. Using RL, the agent will learn quickly that moving +right is preferable to moving left. + +### The Left-Right Agent + +Given the description above, let’s begin creating the Left-Right agent. +This agent will have a *move* operator that will choose between moving +left and right. Because the agent does not know a priori which direction +is better, the agent will be indifferent as to the preference between +these actions. As you are unfamiliar with the particulars of RL agent +design, either type the following code into your favorite editor or open +the VisualSoar *left-right* project in the *Agents* directory: + +Initialization + +The agent stores directions and associated reward on the state + +```Soar +sp {propose\*initialize-left-right +(state ^superstate nil + +-^name) +--> +( ^operator +) +( ^name initialize-left-right) + +} +``` + +```Soar +sp {apply\*initialize-left-right +(state ^operator ) +( ^name initialize-left-right) +--> +( ^name left-right +^direction +^location start) +( ^name left ^reward -1) +( ^name right ^reward 1) + +} +``` + +Move + +The agent can move in any available direction. The chosen direction is +stored on the state. + +```Soar +sp {left-right*propose*move +(state ^name left-right +^direction.name +^location start) +--> +( ^operator +) +( ^name move +^dir ) + +} +``` + +```Soar +sp {left-right\*rl\*left +(state ^name left-right +^operator +) +( ^name move +^dir left) +--> +( ^operator = 0) + +} +``` + +```Soar +sp {left-right\*rl\*right +(state ^name left-right +^operator +) +( ^name move +^dir right) +--> +( ^operator = 0) + +} +``` + +```Soar +sp {apply*move +(state ^operator +^location start) +( ^name move +^dir ) +--> +( ^location start - ) +(write (crlf) |Moved: | ) + +} +``` + +Reward + +When an agent chooses a direction, it is afforded the respective reward. + +```Soar +sp {elaborate\*reward +(state ^name left-right +^reward-link +^location +^direction ) +( ^name ^reward ) +--> +( ^reward.value ) + +} +``` + +Conclusion + +When an agent chooses a direction, the task is over and the agent halts. + +```Soar +sp {elaborate\*done +(state ^name left-right +^location {<> start}) +--> +(halt) + +} +``` + +You will notice a number of unexpected elements in the code above, +namely the *rl* rules for the *move* operator and the reward +elaboration. The reasons for these components will be made clear in +later sections. + +### Running the Left-Right Agent + +Start the Soar Debugger and load the source for the Left-Right agent. By +default, reinforcement learning is disabled. To enable this learning +mechanism, enter the following commands: + +rl --set learning on + +indifferent-selection –-epsilon-greedy + +Note that these commands have been added to the *_firstload* file of +the included *left-right* project. The first command turns learning on, +while the second sets the exploration policy (more on this later.) + +Next, click the “Step" button. This will run Soar through the first +cycle. You will note initialization has been chosen, no surprise. In the +debugger, execute the following command: + +print --rl + +This command shows you the numerical indifferent preferences in +procedural memory subject to RL updating. The output is presented here: + +left-right\*rl\*right 0. 0 + +left-right\*rl\*left 0. 0 + +This result shows that the preference for the two operator instances +after 0 updates have a value of 0. Click “Step” two more times, then +execute *print --rl* again, to see RL in action: + +left-right\*rl\*right 1. 0.3 + +left-right\*rl\*left 0. 0 + +After applying the move operator, the numerical indifference value for +the rule associated with moving right has now been updated 1 time to a +value of 0.3. Note that since the move preferences are indifferent, and +thus the decision process is made probabilistically, your agent may have +decided to move left instead of right. In this case the +*left-right\*rl\*left* preference would have been updated 1 time with a +value of -0.3. + +Now click the “Init-soar” button. This will reinitialize the agent. +Execute *print --rl*. Notice that the numeric indifferent values have +not changed from the previous run. Storing these values between runs is +the method by which RL agents learn. Run the agent 20 more times, +clicking the “Init-soar” button after each halted execution. You should +notice the numeric indifference value for moving right increasing, while +the value for moving left decreases. Correspondingly, you should notice +the agent choosing to move left less frequently. + +### Building a Learning Agent + +Conversion of most agents to take advantage of reinforcement learning +takes part in three stages: (1) use RL rules, (2) implement one or more +reward rules, and (3) enable the reinforcement learning mechanism. As an +example, we will update the basic Water-Jug agent from Tutorial Part I +to take advantage of RL functionality. The modified code can be found in +the *Agents* directory. + +### RL Rules + +Rules that are recognized as updateable by the RL mechanism must abide +by a specific syntax: + +```Soar +sp {my\*proposal\*rule +(state ^operator + +^condition ) +--> +( ^operator = 2.3) + +} +``` + +The name of the rule can be anything and the left-hand side (LHS) of the +rule, the conditions, may take any form. However, the right-hand side +(RHS) must take the following form: +( ^operator = number) + +To be specific, the RHS can only have one action, asserting a single +numeric indifferent preference, and *number* must be a numeric constant +value (such as *2.3* in the example above). Any other actions, including +proposing acceptability of the operator, must take place in a separate +rule. + +Recalling the Water Jug problem, our goal will be to have the agent +learn the best conditions under which to empty a jug (of particular +volume), fill a jug (of particular volume), and pour one jug (of +particular volume) to another. Thus we will modify the *empty*, *fill*, +and *pour* operators to afford them RL updatable rules. + +Modifying the Water-Jug agent’s rules to make them compatible with RL +will take two steps: (a) modify the existing proposal rules and (b) +creating new RL rules. Modification of the existing proposal rule is +trivial: simply remove the “=” (equal) sign from the operator preference +assertion action on the RHS: + +```Soar +sp {water-jug*propose*empty +(state ^name water-jug +^jug ) +( ^contents > 0) +--> +( ^operator +) +( ^name empty +^empty-jug )} +``` + +```Soar +sp {water-jug*propose*fill +(state ^name water-jug +^jug ) +( ^empty > 0) +--> +( ^operator +) +( ^name fill +^fill-jug )} +``` + +```Soar +sp {water-jug*propose*pour +(state ^name water-jug +^jug { <> }) +( ^contents > 0 ) +( ^empty > 0) +--> +( ^operator +) +( ^name pour +^empty-jug +^fill-jug )} +``` + +To be clear, these modified rules propose their respective operators +with an acceptable preference. Next, we will write RL rules whose +conditions detect these acceptable preferences and compliment with +numeric indifferent preferences. + +The second step of agent modification can be much more laborious. In +order for RL to provide feedback for each action in each state of the +problem, it must have an RL rule for each state-action pair. In the +Water Jug problem, a state can be represented by the volume of each of +the jugs and the action (empty, fill, or pour) with one of the two jugs. +As an example, one RL rule for the emptying the 3-unit jug (currently +storing 2 units) when the 5-unit jug has 4 units could be written as +follows: + +```Soar +sp {water-jug*empty\*3\*2\*4 +(state ^name water-jug +^operator + +^jug ) +( ^name empty +^empty-jug.volume 3) +( ^volume 3 +^contents 2) +( ^volume 5 +^contents 4) +--> +( ^operator = 0) + +} +``` + +For simple agents, like the Left-Right agent above, enumerating all +state-action pair as RL rules by hand is plausible. However, the +Water-Jug agent requires $$ (3 * 2 * 4 * 6) = 144\ RL $$ rules to fully +represent this space. Since we can express these rules as the output of +a simple combinatorial pattern, we will use the Soar *gp* command to +generate all the rules we need: + +gp {rl\*water-jug*empty +(state ^name water-jug +^operator + +^jug ) +( ^name empty +^empty-jug.volume \[3 5\]) +( ^volume 3 +^contents \[0 1 2 3\]) +( ^volume 5 +^contents \[0 1 2 3 4 5\]) +--> +( ^operator = 0) + +} +``` + +gp {rl\*water-jug*fill +(state ^name water-jug +^operator + +^jug ) +( ^name fill +^fill-jug.volume \[3 5\]) +( ^volume 3 +^contents \[0 1 2 3\]) +( ^volume 5 +^contents \[0 1 2 3 4 5\]) +--> +( ^operator = 0) + +} +``` + +gp {rl\*water-jug*pour +(state ^name water-jug +^operator + +^jug ) +( ^name pour +^empty-jug.volume \[3 5\]) +( ^volume 3 +^contents \[0 1 2 3\]) +( ^volume 5 +^contents \[0 1 2 3 4 5\]) +--> +( ^operator = 0) + +} +``` + +Note that had the rules required a more complex pattern for generation, +or had we not known all required rules at agent design time, we would +have made use of rule *templates* (see the Soar Manual for more +details). + +### Reward Rules + +Reward rules are just like any other Soar rule, except that they modify +the *reward-link* structure of the state to reflect reward associated +with the agent’s current operator decision. Reward values must be stored +on the *value* element of the *reward* attribute of the *reward-link* +identifier (state.reward-link.reward.value). + +Of significant note, Soar does not remove or modify structures within +the *reward-link*, including old reward values. It is the agent’s +responsibility to maintain the *reward-link* structure to reflect proper +feedback to the RL mechanism. In most cases, this means reward rules +will be i-supported, such as to create non-persistent reward values. If +an attribute remains on the *reward-link* structure, such as through an +o-supported rule, the reward will count multiple times in the +reinforcement learning. + +For the Water-Jug agent, we will provide reward only when the agent has +achieved the goal. This entails making a minor modification to the +goal-test rule: + +```Soar +sp {water-jug*detect*goal\*achieved +(state ^name water-jug +^jug +^reward-link ) +( ^volume 3 ^contents 1) +--> +(write (crlf) |The problem has been solved.|) +( ^reward.value 10) +(halt)} +``` + +Now load this code into the debugger and run it a few times (if loading +your own code, remember to enable reinforcement learning). After about +five runs you should find that the agent has adopted a near optimal +strategy. At any point during the runs you can execute the *print --rl* +command to see the numeric indifferent values of the RL rules generated +by the *gp* command. You can right-click and print any of these rules to +see their conditions. + +### Further Exploration + +Consider the following output from a run (watch level 0) of the learning +left-right agent from section 1: + +run + +Moved: right + +This Agent halted. + +An agent halted during the run. + +init-soar + +Agent reinitialized. + +run + +Moved: right + +This Agent halted. + +An agent halted during the run. + +init-soar + +Agent reinitialized. + +run + +Moved: left + +This Agent halted. + +An agent halted during the run. + +You should notice that at run 3 moving left is selected. By this point +moving right has an obvious advantage in numerical preference values, +thus why is left chosen? The answer lies with exploration policies. + +There are times in learning when exploration of operations currently +considered less-than-preferred may lead you down a useful path. Soar +allows you to tune your level of exploring these alternate paths using +the *indifferent-selection* command. + +In the Soar Debugger, type “*indifferent-selection --stats”* (sans +quotes). The result should look like this: + +Exploration Policy: epsilon-greedy + +Automatic Policy Parameter Reduction: off + +epsilon: 0.1 + +epsilon Reduction Policy: exponential + +epsilon Reduction Rate (exponential/linear): 1/0 + +temperature: 25 + +temperature Reduction Policy: exponential + +temperature Reduction Rate (exponential/linear): 1/0 + +This command prints the current exploration policy as well as a number +of tuning parameters. There are five exploration policies: *boltzmann*, +*epsilon-greedy*, *softmax*, *first*, and *last*. You can change the +exploration policy by issuing the following command (where +“policy_name” should be replaced with one of the policies above): + +indifferent-selection --policy_name + +This tutorial will only discuss the *epsilon-greedy* policy. For +information on the other policies you should read the Soar manual. +Epsilon greedy is a policy common within reinforcement learning +experimentation to allow parameter-controlled exploration of operators +not currently recognized as most preferred. This policy is controlled by +the *epsilon* parameter. The policy is summarized as such: + +With ( 1 - epsilon ) probability, the most preferred operator is to be +chosen. With epsilon probability, a random selection of all indifferent +operators is made. + +When Soar is first started, the default exploration policy is *softmax*. +However, the first time RL is enabled, the architecture automatically +changes the exploration policy to *epsilon-greedy*, a policy more +suitable for RL agents. The default value of *epsilon* is 0.1, dictating +that 90% of the time the operator with greatest numerical preference +value is chosen, while the remaining 10% of the time a random selection +is made from all acceptable proposed operators. You can change the +*epsilon* value by issuing the following command: + +indifferent-selection --epsilon + +Acceptable values for *epsilon* are numbers between 0 and 1 (inclusive). +You may note, by the definition, that a value of 0 will eliminate the +chance of exploration and a value of 1 will result in a uniformly random +selection. + +With this explanation, you should experiment with different values of +epsilon during different runs in the agents discussed in this tutorial. diff --git a/docs/tutorials/soar_tutorial/07.md b/docs/tutorials/soar_tutorial/07.md new file mode 100644 index 00000000..8188e5e4 --- /dev/null +++ b/docs/tutorials/soar_tutorial/07.md @@ -0,0 +1,458 @@ +# Part VII: Semantic Memory + +Semantic memory (SMem) in Soar is a mechanism that allows agents to +deliberately store and retrieve objects that are persistent. This +information supplements what is contained in short-term working memory +and other long-term memories, such as rules in procedural memory. + +### The Semantic Store + +Before we delve into how an agent can use semantic memory, let’s see an +example of preloading knowledge and viewing the contents of the memory. + +First, open the Soar Debugger. Then, execute the following command (this +can be loaded from a source file just as any other Soar command): + +smem --add { +( ^name alice ^friend ) +( ^name bob ^friend ) +( ^name charley) + +} + +```` + +As we shall see in a moment, executing this command adds three objects +to semantic memory. In general, the *smem --add* command is useful to +preload the contents of large knowledge bases in Soar. + +We can view the contents of semantic memory using the following command: + +print @ + +Which will output the following result: +(@1 ^friend @2 ^name alice \[+0.000\]) +(@2 ^friend @1 ^name bob \[+0.000\]) +(@3 ^name charley \[+0.000\]) + +Note first that the variables from the *smem --add* command have been +instantiated as specific identifiers ( as @1, as @2, and + as @3). All identifiers in semantic memory are persistent, and +thus we call them *long-term identifiers* (or LTIs), in contrast to all +other identifiers, which are short-term. When printed, long-term +identifiers are prefixed by the @ symbol and, when depicted, are shown +using a double circle. The number in square brackets is the bias value +of the object, used to break ties during retrievals, a topic to which we +shall return later. Finally, unlike working memory and rules, the +knowledge in semantic memory need not be connected, nor linked directly +or indirectly, to a state. + +To pictorially view the contents of semantic memory, we can use the +visualize command to render the contents of semantic memory to an image. +For example, execute the following command: + +visualize smem + +If you have graphviz and DOT installed (see for +more detail), it should launch your system viewer to show a diagram +similar to: + +![](Images/07/image1.png) + +Now that we have seen the contents of semantic memory, you can confirm that none +of this knowledge is present in any of the agent’s other memories. For instance, +execute the following commands to print the contents of working and procedural +memories: + +print --depth 100 + +print + +You notice that the contents of the semantic store can be completely +independent of the other memories, though, as discussed later, an agent +can access and modify the store over time. + +We are now done with this example and wish to clear the semantic store. +To do this we issue a special command: + +smem --clear + +The agent is now reinitialized, as you can verify by printing the +contents of working memory, procedural memory, and now semantic memory. + +### Agent Interaction + +Agents interact with semantic memory via special structures in working +memory. Soar automatically creates an *smem* link on each state, and +each *smem* link has specialized substructure: a *command* link for +agent-initiated actions and a *result* link for feedback from semantic +memory. For instance, issue the following command: + +print --depth 10 + +If you read the output carefully you will notice a WME that can be +generally represented as ( ^smem ) and two additional +WMEs that can be represented as ( ^command ) and +( ^result ). + +As described in the following sections, the agent, via rules, populates +and maintains the *command* link and the architecture populates and +cleans up the *result* link. + +For the agent to interact with semantic memory, this mechanism must be +enabled. By default, all learning mechanisms in Soar are disabled. To +enable semantic memory, issue the following command: + +smem --enable + +### Agent Storage and Modification + +An agent stores an object to semantic memory by issuing a *store* +command. The syntax of a store command is ( ^store ) where + is the *command* link of a state and is an identifier. + +An agent can issue multiple store commands simultaneously, and the +commands are processed at the end of the phase in which they are issued. +A *store* command is guaranteed to succeed and the response from the +architecture will be a success WME: ( ^success ), where +is the *result* link of the state on which the *store* command was +issued and was the value of the *store* command. + +A *store* command stores the identifier that is the result of the +command, as well as any augmentations of that identifier. The command is +not recursive. If the identifier to be stored was not long-term, it is +changed in place to a long-term identifier. If it was already in +semantic memory, the augmentations of the long-term identifier in +semantic memory are overridden. + +Let’s see an example. Source the following rules into the Soar Debugger +(they are available in the *smem-tutorial.soar* file within the *Agents* +directory). + +```Soar +sp {propose\*init +(state ^superstate nil + +-^name) +--> +( ^operator +) +( ^name init)} +```` + +```Soar +sp {apply\*init +(state ^operator.name init +^smem.command ) +--> +( ^name friends) +( ^store ) +( ^name alice ^friend ) +( ^name bob ^friend ) +( ^name charley)} +``` + +```Soar +sp {propose\*mod +(state ^name friends +^smem.command ) +( ^store ) +( ^name alice) +( ^name bob) +( ^name charley) +--> +( ^operator +) +( ^name mod)} +``` + +```Soar +sp {apply\*mod +(state ^operator.name mod +^smem.command ) +( ^store ) +( ^name alice) +( ^name bob) +( ^name charley) +--> +( ^name alice -) +( ^name anna +^friend ) +( ^store -) +( ^store -)} +``` + +Now click the “Step” button to run till the decision phase and notice +that the _init_ operator is selected. Now, click the “Watch 5” button +and then the “Run 1 -p” button to watch as the operator is applied. +Below is part of the trace that should be produced. If you do not see +this part of this trace in your run, be sure that you enabled semantic +memory (see section above). +--- apply phase --- +--- Firing Productions (PE) For State At Depth 1 --- + +Firing apply\*init + +\+ (C3 ^name charley + :O ) (apply\*init) + +\+ (B1 ^friend A1 + :O ) (apply\*init) + +\+ (B1 ^name bob + :O ) (apply\*init) + +\+ (A1 ^friend B1 + :O ) (apply\*init) + +\+ (A1 ^name alice + :O ) (apply\*init) + +\+ (C2 ^store C3 + :O ) (apply\*init) + +\+ (C2 ^store B1 + :O ) (apply\*init) + +\+ (C2 ^store A1 + :O ) (apply\*init) + +\+ (S1 ^name friends + :O ) (apply\*init) +--- Change Working Memory (PE) --- + +\=>WM: (25: C3 ^name charley) + +\=>WM: (24: B1 ^friend A1) + +\=>WM: (23: B1 ^name bob) + +\=>WM: (22: A1 ^friend B1) + +\=>WM: (21: A1 ^name alice) + +\=>WM: (20: C2 ^store A1) + +\=>WM: (19: C2 ^store B1) + +\=>WM: (18: C2 ^store C3) + +\=>WM: (17: S1 ^name friends) + +Notice that the _apply\*init_ rule fired and added 3 _store_ commands to +working memory, where the identifiers to be stored are, initially, not +long-term, and whose augmentations mirror the contents of the _smem +--add_ command in Part 1 of this tutorial. Then, at the end of the +elaboration phase, semantic memory processed the command, converted the +identifiers to long-term, and added status for each command. + +Now, try printing the contents of semantic memory using the _print @_ +command. You will see that semantic memory now has the same contents as +after using the _smem --add_ command in Part 1. + +Application of the next operator modifies the contents of semantic +memory by overriding the contents of an existing long-term identifier +(@1). Click the “Step” button to select the next operator (_mod_) and +then click the “Run 1 -p" button to apply the operator: + +Firing apply\*mod + +- (A1 ^name alice + :O ) (apply\*init) + +- (C2 ^store B1 + :O ) (apply\*init) + +- (C2 ^store C3 + :O ) (apply\*init) + +\+ (A1 ^friend C3 + :O ) (apply\*mod) + +\+ (A1 ^name anna + :O ) (apply\*mod) +--- Change Working Memory (PE) --- + +\=>WM: (33: A1 ^name anna) + +\=>WM: (32: A1 ^friend C3) + +<=WM: (21: A1 ^name alice) + +<=WM: (18: C2 ^store C3) + +<=WM: (19: C2 ^store B1) + +You will notice in the trace that the store commands for @2 and @3 are +removed by the application rule, and that augmentations of @1 are +removed and added. Then, at the end of the elaboration phase, semantic +memory cleans up the status information for the old _store_ commands. + +Now, print the contents of semantic memory using the _print @_ command: +(@1 ^friend @2 @3 ^name anna \[+1.000\]) +(@2 ^friend @1 ^name bob \[+1.000\]) +(@3 ^name charley \[+1.000\]) + +Notice that the augmentations of @1 have indeed changed in semantic +memory to reflect the new _store_ command, while @2 and @3 remain +unchanged. + +### Non-Cue-Based Retrieval + +The first way an agent can retrieve knowledge from semantic memory is +called a non-cue-based retrieval: the agent requests from semantic +memory all of the augmentations of a known long-term identifier. The +syntax of the command is ( ^retrieve ) where is a +short-term identifier that is linked to a long-term identifier. In other +words, it is a short-term identifier that was previously used in a store +command or recalled via a retrieve or query command. + +As an example, add the following three rules to our agent from Part 3 of +this tutorial (these rules are already part of the _smem-tutorial.soar_ +file in the _Agents_ directory): + +```Soar +sp {propose\*ncb-retrieval +(state ^name friends +^smem.command ) +( ^store ) +( ^name anna +^friend ) +--> +( ^operator + =) +( ^name ncb-retrieval +^friend )} +``` + +```Soar +sp {apply\*ncb-retrieval\*retrieve +(state ^operator +^smem.command ) +( ^name ncb-retrieval +^friend ) +( ^store ) +--> +( ^store - +^retrieve )} +``` + +```Soar +sp {apply\*ncb-retrieval\*clean +(state ^operator +^smem.command ) +( ^name ncb-retrieval +^friend ) +( ^ ) +--> +( ^ -)} +``` + +These rules retrieve all the information about one of @1’s two friends +(selected randomly) and remove the friend’s augmentations (such as name +and/or friend) from working memory. + +Unlike _store_ commands, all retrievals are processed during the agent’s +output phase and only one retrieval command can be issued per state per +decision. + +Now click the “Step” button and notice that one of the two _ncb_ +operators is selected. Click “Run 1 -p" to see the application rule +create a _retrieve_ command, requesting information about one of the two +friends, as well as remove that friend’s augmentations from working +memory. Then click the “Run 1 -p" button again to proceed through the +output phase. Finally, print the full contents of the _smem_ link +(_print --depth 10 L1_): +(L1 ^command C2 ^result R3) +(C2 ^depth 3 ^retrieve B1 (@2)) +(R3 ^retrieved L2 (@2) ^success B1 (@2)) +(L3 ^friend L2 (@2) ^friend L4 (@3) ^name anna) +(L2 ^friend L3 (@1) ^name bob) +(L4 ^name charley) + +We see that semantic memory has retrieved and added to working memory +the name of the friend, as well as indicated status for this command +(_success_). Your run may have retrieved @3 instead, as a result of the +random selection process: + +Note that had the _retrieve_ command been issued with an identifier that +was not linked to a long-term identifier, the status would have been +_failure_ and there would be no _retrieved_ structure. Note also that +retrieved knowledge is limited to the augmentations of the long-term +identifier: like the _store_ command, the _retrieve_ command is not +recursive. + +### Cue-Based Retrieval + +The second way an agent can retrieve knowledge from semantic memory is +called a cue-based retrieval: the agent requests from semantic memory +all of the augmentations of an unknown long-term identifier, which is +described by a subset of its augmentations. The syntax of the command is +( ^query ), where the desired augmentations all have + as their identifier. + +The augmentations of the cue form hard constraints, based upon the value +of each WME. If the value of the WME is a constant (string, integer, or +float) or long-term identifier, then any retrieval is required to have +exactly the attribute/value pair specified. If the value of the WME is a +short-term identifier, then any retrieval is required to have an +augmentation that has the same attribute, but the value is +unconstrained. + +As an example, add the following two rules to our agent from Part 4 of +this tutorial (these rules are already part of the _smem-tutorial.soar_ +file in the _Agents_ directory): + +```Soar +sp {propose\*cb-retrieval +(state ^name friends +^smem.command ) +( ^retrieve) +--> +( ^operator + =) +( ^name cb-retrieval)} +``` + +```Soar +sp {apply\*cb-retrieval +(state ^operator +^smem.command ) +( ^name cb-retrieval) +( ^retrieve ) +--> +( ^retrieve - +^query ) +( ^name +^friend )} +``` + +These rules retrieve an identifier that meets two constraints: (1) it +has an augmentation where the attribute is “name”, but the value can be +any symbol, and (2) it has an augmentation where the attribute is +“friend” and the value is the long-term identifier retrieved as a +result of applying the operator in Part 3. + +As a reminder, all retrievals are processed during the agent’s output +phase and only one retrieval command can be issued per state per +decision. + +So now click the “Step” button and then click the “Run 1 -p" to see the +application rule create a _query_ command, as well as remove the +previous _retrieve_ command from working memory. Then click the “Run 1 +-p" button again to proceed through the output phase. Finally print the +contents of the _smem_ link (_print --depth 10 L1_): +(L1 ^command C2 ^result R3) +(C2 ^depth 3 ^query C4) +(C4 ^friend B1 (@2) ^name A2) +(R3 ^retrieved L5 (@1) ^success C4) +(L7 ^name charley) +(L6 ^friend L5 (@1) ^name bob) +(L5 ^friend L6 (@2) ^friend L7 (@3) ^name anna) + +We see that semantic memory has retrieved and added to working memory +the identifier @1 and all of its augmentations, as well as indicated +status for this command (_success_). If in Part 4 of this tutorial your +agent retrieved @3, you may have slightly different output. + +Note that had no long-term identifier in semantic memory satisfied the +constraints of the _query_ command cue, the status would have been +_failure_ and there would be no retrieved structure. Note also that +retrieved knowledge is limited to the augmentations of the long-term +identifier: like the store command, retrievals are not recursive. We see +this in the outputs above as one friend has augmentations (as a result +of the _retrieve_ command in Part 4), whereas the other does not. + +If multiple identifiers had satisfied the constraints of the cue (such +as if the cue had only a WME with “name” as the attribute and a +short-term identifier as the value), then the long-term identifier with +the largest bias value is returned. By default, the bias value is a +monotonically increasing integer, reflecting the recency of the last +storage or retrieval of an object. + +It is also possible to _prohibit_ one or more long-term identifiers from +being retrieved. For more information on this any many additional +capabilities of semantic memory, read the Semantic Memory chapter of the +Soar Manual. diff --git a/docs/tutorials/soar_tutorial/08.md b/docs/tutorials/soar_tutorial/08.md new file mode 100644 index 00000000..67ed62d6 --- /dev/null +++ b/docs/tutorials/soar_tutorial/08.md @@ -0,0 +1,429 @@ +# Part VIII: Episodic Memory + +Episodic memory (EpMem) in Soar is a mechanism that automatically +captures, stores, and temporally indexes agent state and supports a +content-addressable agent interface to retrieve this autobiographical +prior experience. This information supplements what is contained in +short-term working memory and other long-term memories, such as rules in +procedural memory. + +## A Short Demonstration + +Before we delve into how an agent can use episodic memory, let’s see an +example of capturing an episode and viewing the contents of the memory. + +First, open the Soar Debugger. Then, execute the following command (this +can be loaded from a source file just as any other Soar command): + +epmem --set trigger dc + +epmem --set learning on + +watch --epmem + +Now, click the “Step” button twice. If we inspect the trace, and ignore +the state no-change impasses, we see the following message: + +NEW EPISODE: 1 + +This is an indication that a new episode, with id 1, has been +automatically stored by the architecture within the episodic store. + +We can view the contents of episodic memory using the _epmem --print_ +command, which expects an episode id as an argument. For example, +execute the following command: + +epmem --print 1 + +Which will output the following result: +( ^io ^reward-link ^superstate nil ^type state) +( ^input-link ^output-link ) + +To pictorially view the contents of semantic memory, we can use the +visualize command to render the contents of semantic memory to an image. +For example, execute the following command: + +```bash +visualize epmem +``` + +If you have graphviz and DOT installed (see for +more detail), it should launch your system viewer to show a diagram +similar to: + +![](Images/08/08/image1.png) + +From both the trace output as well as the Graphviz rendering we can see +that episodic memory has stored most of the top-state of the agent’s +working memory at a particular moment in time. In the following sections +we’ll examine in more detail how to control automatic storage and how +agents can retrieve episodic knowledge. + +### Episodic Storage + +As we saw in Part 1 of this tutorial, episodic storage is automatic and +captures the top state of the agent’s working memory. To enable storage, +episodic memory must be enabled. By default, all learning mechanisms in +Soar are disabled. To enable episodic memory, issue the following +command: + +```bash +epmem --set learning on +``` + +There are a few architectural parameters that are important to control +episodic storage. The first is the event that triggers storage. By +default, episodic memory stores new episodes whenever a WME is added to +working memory that has the _output-link_ as its identifier. However, +Soar also supports storing episodes each decision cycle (“dc”), which is +enabled using the following command (which we used in Part 1 of this +tutorial): + +```bash +epmem --set trigger dc +``` + +The next important parameter is the phase during which episodic memory +stores episodes (and processes retrievals, as discussed later). By +default, this processing occurs at the end of the _output_ phase. +However, Soar also supports this processing occurring at the end of the +_decision_ phase, which is enabled using the following command: + +```bash +epmem --set phase selection +``` + +Finally, it is sometimes the case that certain portions of the agent’s +working memory should be excluded from automatic storage. Episodic +memory supports specifying a set of excluded attributes: if automatic +storage encounters an excluded attribute during a breadth-first walk of +working memory, it does not store that WME, nor any substructure if it +was the case that the value of the WME was an identifier. To view the +current excluded set, issue the following command: + +```bash +epmem --get exclusions +``` + +To change the excluded set, issue the following command: + +```bash +epmem --set exclusions +``` + +This command toggles the state of an _attribute_ within the set: thus if +this command is executed with an _attribute_ that is already in the +excluded set, it is removed from the set, otherwise it is added. By +default, “epmem” and “smem” are in the excluded set, which is why we do +not see these architectural links in the trace/visualization in Part 1 +of this tutorial. + +In Part 1, we also enabled trace output that is useful for understanding +episodic memory via the following command: + +```bash +watch --epmem +``` + +This trace option indicates when new episodes are recorded, as well as +debugging information for retrievals, as discussed later. + +### Agent Interaction + +Agents interact with episodic memory via special structures in working +memory. Soar automatically creates an _epmem_ link on each state, and +each _epmem_ link has specialized substructure: a _command_ link for +agent-initiated actions and a _result_ link for feedback from episodic +memory. For instance, issue the following command: + +```bash +print --depth 10 +``` + +If you read the output carefully you will notice a WME that can be +generally represented as ( ^epmem ) and three +additional WMEs that can be represented as ( ^command ), +( ^result ), and ( ^present-id ) + +As described in the following sections, the agent, via rules, populates +and maintains the _command_ link and the architecture populates and +cleans up the _result_ link. As episodes are stored, the _present-id_ +augmentation updates to indicate the current episode id, the value of +which is a positive integer. + +For the agent to interact with episodic memory, this mechanism must be +enabled. As mentioned in Part 2, by default, all learning mechanisms in +Soar are disabled and so you must enable episodic memory via the command +in Part 2. + +By default, all commands are processed during the agent’s output phase +(this can be changed using the _phase_ parameter, as described in Part 2 +of this tutorial) and only one command can be issued per state per +decision. + +### Cue-Based Retrieval + +The primary method that an agent can retrieve knowledge from episodic memory is +called a cue-based retrieval: the agent requests from episodic memory an episode +that most closely matches a cue of working-memory elements. The syntax of the +command is ( ^query ), where forms the root of the cue. +Conceptually, episodic memory compares the cue to all episodes in the store, +scoring each one, and returns the most recent episode with the maximal score. + +Episodes are scored based upon the _leaf_ WMEs in the cue. A leaf WME has either +a value that is a constant, a long-term identifier, or a short-term identifier +with no augmentations. A leaf WME is _satisfied_, with respect to a particular +episode, if there exists a _path_, or sequence of WMEs, from the episode root to +that leaf WME, where the attributes of all intermediate WMEs exactly match those +in the cue, and short-term identifiers in the cue variablize to consistently +match identifiers in the episode. This is similar to how variables in the +conditions of rules bind to specific identifiers in working memory. However, as +discussed below, episode scoring is disjunctive with respect to leaf WMEs (i.e. +each leaf WME is considered independently), whereas rule matching is conjunctive +with respect to production conditions (i.e. a rule matches only if _all_ +conditions are satisfied). By default, the score of an episode is simply the +number of satisfied leaf WMEs. + +Let us consider an example cue, composed of the following WMEs, where N1 +is the value of the _query_ command, as described above: +(N1 ^feature value +^id N2) +(N2 ^sub-feature value2 +^sub-id N3) + +Or, visually: + +![](Images/08/image2.png) + +This cue has three leaf WMEs: (N1 ^feature value), (N2 ^sub-feature +value2), and (N2 ^id N3). Now consider the following episode: + +![](Images/08/image3.png) + +The first leaf WME of the cue, (N1 ^feature value), is not satisfied by this +episode, as there is no (E1 ^feature value) WME: (E1 ^feature2 value) has a +different attribute and (E1 ^feature value3) has a different value. Both other +leaf WMEs, however, are satisfied. (N2 ^sub-feature value2) is satisfied by +variablizing E1 as N1 and E2 as N2: (E1 ^id E2) and (E2 ^sub-feature value2). +(N2 ^id N3) is satisfied by variablizing E1 as N1, E3 as N2, and E5 as N3: (E1 +^id E3), (E3 ^sub-id E5). Note that the substructure of E4 in the episode +matches that of N2 in the cue, but there is no WME (E1 ^id E4), and so E4 is not +considered. Thus, this episode, with respect to the cue, has a score of 2. + +Note, however, that it is not possible to _unify_ the cue with the episode: +there is no single identifier in the episode that, when bound as N2 in the cue, +satisfies both (N2 ^sub-feature value2) and (N2 ^sub-id N3). If an episode gets +a perfect score, such that all leaf WMEs are satisfied, episodic memory attempts +to graph match the cue with the episode (i.e. determine if there exists an +isomorphism between the cue and the episode). So in response to a cue-based +retrieval command, episodic memory will return the most recent graph-matched +episode, or, if one does not exist, the most recent episode with the maximal +episode score. For clarity, episode recency is directly proportional to the +episode id, where larger episode id’s are more recent. + +Let’s see how the example above works in Soar. Run the Soar Debugger and source +the following rules (these rules are already part of the epmem-tutorial.soar +file in the _Agents_ directory): + +```Soar +sp {propose\*init +(state ^superstate nil +-^name) +--> +( ^operator + =) +( ^name init)} +``` + +```Soar +sp {apply\*init +(state ^operator ) +( ^name init) +--> +( ^name epmem +^feature2 value +^feature value3 +^id +^id +^other-id ) +( ^sub-feature value2) +( ^sub-id ) +( ^sub-id +^sub-feature value2)} +``` + +```Soar +sp {epmem*propose*cbr +(state ^name epmem + +-^epmem.command.) +--> +( ^operator + =) +( ^name cbr)} +``` + +```Soar +sp {epmem*apply*cbr-clean +(state ^operator +^feature2 +^feature +^id +^id +^other-id ) +( ^sub-feature value2) +( ^sub-id) +( ^name cbr) +--> +( ^feature2 - +^feature - +^id - +^id - +^other-id -)} +``` + +```Soar +sp {epmem*apply*cbr-query + (state ^operator + ^epmem.command ) + ( ^name cbr) +--> + ( ^query ) + ( ^feature value + ^id ) + ( ^sub-feature value2 + ^sub-id )} +``` + +Now execute the following commands: + +```bash +epmem --set trigger dc +epmem --set learning on +watch --epmem +``` + +Then click the “Step” button and then the “Run 1 -p” button. Now print +out the top state of working memory (_print --depth 10 s1_). Notice that +the top state contains the structures of the sample episode above (such +as _^feature value_), as well as other WMEs (such as _^superstate nil_). + +Now click the “Step” button. You should notice in the trace that episode +\#1 was stored. Click the “Run 1 -p” button to apply the _cbr_ operator +and print the top state of working memory (_print --depth 10 s1_). +Notice that the structures of the sample episode have been removed and +that the sample cue has been added to the _command_ structure of the +_epmem_ link. + +Now click the “Run 1 -p” button. Episodic memory stored another episode +(\#2) and then processed the cue-based query. The trace contains the +following text: + +CONSIDERING EPISODE (time, cardinality, score): (1, 2, 2.000000) + +NEW KING (perfect, graph-match): (false, false) + +The first line indicates that episodic memory compared the cue to +episode \#1 (i.e. time=1), found that the cardinality of the set of +satisfied leaf WMEs was 2, and thus the episode was scored as 2. Since +this was the first considered episode, it is indicated as “king” \[of +the mountain\]. However, since the episode did not have a perfect score +(2 out of 3), graph-match was not attempted and was thus not successful. +Since episode \#2 did not have any features in common with the cue +(application of the _cbr_ operator removed these structures), episodic +memory did not consider it as a performance optimization. + +Now print the full contents of the episodic memory link: + +```bash +$ print --depth 10 e1 +(E1 ^command C1 ^present-id 3 ^result R2) +(C1 ^query N1) +(N1 ^feature value ^id N2) +(N2 ^sub-feature value2 ^sub-id N3) +(R2 ^cue-size 3 ^graph-match 0 ^match-cardinality 2 +^match-score 2.^memory-id 1 +^normalized-match-score 0.6666666666666666 ^present-id 3 +^retrieved R4 ^success N1) +(R4 ^feature value3 ^feature2 value +^id I5 ^id I6 ^io I4 ^name epmem +^operator\* O5 ^other-id O4 ^reward-link R5 +^superstate nil ^type state) +(I5 ^sub-feature value2) +(I6 ^sub-id S3) +(I4 ^input-link I7 ^output-link O6) +(O5 ^name cbr) +(O4 ^sub-feature value2 ^sub-id S4) +``` + +The _result_ structure indicates that the retrieval was successful, has +a link to the full episode contents (rooted at R4), and has meta-data +about the cue-matching process, with respect to the retrieved episode. +Details of these augmentations are in the Episodic Memory chapter of the +Soar Manual. Note that a WME with an “operator\*” attribute (such as: R4 +^operator\* R5) in a retrieved episode represents an acceptable +preference WME in the original episode. + +There are optional modifiers to cue-based queries, including the ability +to prohibit specific episodes from being retrieved and indicating +features that are not desirable in the retrieved episode. More +information on this functionality is in the Episodic Memory chapter of +the Soar Manual. + +### Temporal Progression + +Another way the agent can gain access to episodes is by retrieving the +episode that came temporally before/after the last episode that was +retrieved. The syntax of these commands, respectively, are ( +^previous ) and ( ^next ), where is any +identifier. + +As an example, add the following rules to our agent from Part 4 of this +tutorial (these rules are already part of the _epmem-tutorial.soar_ file +in the _Agents_ directory): + +```Soar +sp {epmem*propose*next +(state ^name epmem +^epmem.command.query) +--> +( ^operator + =) +( ^name next)} +``` + +```Soar +sp {epmem*apply*next +(state ^operator +^epmem.command ) +( ^name next) +( ^query ) +--> +( ^query - +^next )} +``` + +These rules will retrieve the episode that temporally proceeds the +episode retrieved in the previous part of this tutorial. + +Click the “Step” button, then the “Run 1 -p” button. Now print the +episodic memory link (_print --depth 10 e1_). Notice that the _query_ +command has been replaced with a _next_ command. Note that the results +of the previous commands are still in working memory: these will be +automatically cleaned by episodic memory when the _next_ command is +processed. + +Now click the “Run 1 -p” button and print the episodic memory link +(_print --depth 10 e1_): +(E1 ^command C1 ^present-id 4 ^result R2) +(C1 ^next N4) +(R2 ^memory-id 2 ^present-id 4 ^retrieved R6 ^success N4) +(R6 ^io I8 ^name epmem ^operator\* O7 ^reward-link R +^superstate nil ^type state) +(I8 ^input-link I9 ^output-link O8) +(O7 ^name next) + +The result structure has been cleaned of old structures and now shows +that the command was successful and episode \#2 was retrieved (with all +of its original contents). + +You now have some basic understanding of using episodic memory. 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Soar Programs](01.md) +- [Part II Simple External Interaction](02.md) +- [Part III Subgoals](03.md) +- [Part IV More Simple Problem Solving](04.md) +- [Part V Planning and Learning](05.md) +- [Part VI Reinforcement Learning](06.md) +- [Part VII Semantic Memory](07.md) +- [Part VIII Episodic Memory](08.md) + +Additional Files: + +- [Tank Eaters Config](TankEatersConfigFile.md) diff --git a/mkdocs.yml b/mkdocs.yml index f99791c2..e316807d 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -107,6 +107,14 @@ nav: - Soar Markup Language: tutorials/SMLQuickStartGuide.md - Official Soar Tutorials: - tutorials/soar_tutorial/index.md + - Part I Simple Soar Programs: tutorials/soar_tutorial/01.md + - Part II Simple External Interaction: tutorials/soar_tutorial/02.md + - Part III Subgoals: tutorials/soar_tutorial/03.md + - Part IV More Simple Problem Solving: tutorials/soar_tutorial/04.md + - Part V Planning and Learning: tutorials/soar_tutorial/05.md + - Part VI Reinforcement Learning: tutorials/soar_tutorial/06.md + - Part VII Semantic Memory: tutorials/soar_tutorial/07.md + - Part VIII Episodic Memory: tutorials/soar_tutorial/08.md - Tank and Eaters Configuration: tutorials/soar_tutorial/TankEatersConfigFile.md - HowTo Guides: - how_to/index.md From a1a5f74db5428f58336620ffcc0366c1d42cdda3 Mon Sep 17 00:00:00 2001 From: Moritz Schmidt Date: Tue, 2 Apr 2024 12:01:08 +0200 Subject: [PATCH 2/2] Minor changes links and style --- docs/tutorials/soar_tutorial/01.md | 96 ++++++------- docs/tutorials/soar_tutorial/02.md | 38 +++-- docs/tutorials/soar_tutorial/03.md | 214 ++++++++++++++++++++--------- docs/tutorials/soar_tutorial/07.md | 117 ++++++++-------- 4 files changed, 272 insertions(+), 193 deletions(-) diff --git a/docs/tutorials/soar_tutorial/01.md b/docs/tutorials/soar_tutorial/01.md index b94bb258..e5362f8e 100644 --- a/docs/tutorials/soar_tutorial/01.md +++ b/docs/tutorials/soar_tutorial/01.md @@ -36,7 +36,7 @@ will be used to run all of your Soar programs. Your version may look somewhat different, depending on the features added since this document was written. -![](Images/01/image1.png) +![The Soar Debugger](Images/01/image1.png) The large window to the left is called the Interaction window (above). During Soar execution, all debugging information and print statements @@ -51,7 +51,7 @@ an agent. When it is time to run your Soar programs, commands can either be entered by typing them into the command box, or by clicking the buttons -![](Images/01/image2.png) +![Debugger Command Interface](Images/01/image2.png) ### Hello-World Rule: English @@ -365,7 +365,7 @@ the pop-up window define the soar type. You need to fill in "soar" you save it as a text file with linefeeds. Soar cannot handle formatted text and text without linefeeds. -# Building Simple Agents Using Operators +## Building Simple Agents Using Operators In this section, you will learn how to use rules to select and apply _operators_. Operators perform actions, either in the world or @@ -480,7 +480,7 @@ hello-world (O1) is then selected (by the decision procedure). After hello-world is selected, apply*hello-world fires and performs the actions of the operator by printing out “Hello World” and halting. -### ![](Images/01/image7.png) +![](Images/01/image7.png) ## Hello-World Operator: Soar Version @@ -571,7 +571,7 @@ the data structures in working memory. You can print out working memory in the Interaction Window using the print command. To print out all of the attributes and values that have s1 as the identifier, first click in command box below the interaction window, and type -print s1 followed by the “Enter” key. The +`print s1` followed by the “Enter” key. The debugger also has additional windows to the right of the Interaction Window that display common structures, such as the current state, the current operator, etc. The topmost window on the right should already @@ -600,8 +600,8 @@ attributes are ordered alphabetically when they are printed, but that is just to make it easier to find a specific attribute. We can examine more of the structure of o1 using the print command. This -time, select the operator tab or type p -o1. The letter p means “print.” Almost all commands in Soar also +time, select the operator tab or type `p +o1`. The letter p means “print.” Almost all commands in Soar also have one or two-character shortcuts. ![](Images/01/image9.png) @@ -746,7 +746,7 @@ water, and the 3 gallon container has 0 gallons of water. Below is a trace of the optimal solution for the problem showing the states and the operators that are applied to each state. -``` +```bash (5:0,3:0) Fill 3 gallon jug (5:0,3:3) Pour 3 gallon jug into 5 gallon jug (5:3,3:0) Fill 3 gallon jug @@ -886,13 +886,11 @@ propose the operator and one to apply the operator. This operator should be proposed only at the beginning, before any task is selected. Thus, the proposal should be: -``` +```Soar water-jug*propose*initialize-water-jug ``` -If no task is selected, - -then propose the initialize-water-jug operator. +If no task is selected, then propose the initialize-water-jug operator. Writing this rule in Soar is a challenge given what you know so far. How can you test that no task has been selected or that no jugs are defined? @@ -960,12 +958,11 @@ initialization application rule adds the name to the state and creates the jugs with contents of 0. The rule needs to add working memory elements for the initial values of the jugs and their attributes. -``` +```Soar water-jug*apply*initialize-water-jug ``` If the initialize water-jug operator is selected, - then create an empty 5 gallon jug and an empty 3 gallon jug. ```Soar @@ -1127,10 +1124,6 @@ differentiates Soar from most other rule-based systems that don’t have rules that automatically retract their results when they no longer match and it gives Soar its own Truth-Maintenance System (TMS). -### - -### - ## Water Jug State Elaboration The initialization operator creates jugs that include the volume and @@ -1155,7 +1148,9 @@ recompute their actions automatically. For the Water Jug problem, the state elaboration will compute the amount of empty space in a jug: +```Soar water-jug*elaborate*empty +``` If the state is named water-jug and a jug can hold volume v and currently has contents c, then add that it has v – c available (empty) @@ -1309,29 +1304,26 @@ operators _must_ change the state in some way so that another operator can be selected. Thus, in our formulation, filling a full jug must be disallowed. Now write English descriptions of the proposals. -``` +```Soar water-jug*propose*fill ``` If the task is water-jug and there is a jug that is not full, - then propose filling that jug. -``` +```Soar water-jug*propose*empty ``` If the task is water-jug and there is a jug that is not empty, - then propose emptying that jug. -``` +```Soar water-jug*propose*pour ``` If the task is water-jug and there is a jug that is not full and the other jug is not empty, - then propose pouring water from the second jug into the first jug. Each of these rules can potentially match either one of the jugs. @@ -1356,10 +1348,13 @@ operator parameters and possible labels are listed below: - The jug being poured into by pour: `^fill-jug `. For example, with this representation, the operator representation for -pouring from jug to would be: +pouring from jug `` to `` would be: + +```Soar ( ^name pour ^empty-jug ^fill-jug ) +``` There are lots of other possibilities and it is not necessary to share the name of the parameters between the pour operators and the others. @@ -1379,7 +1374,7 @@ sp {water-jug*propose*fill ^fill-jug )} ``` -This rule tests that jug does not have an empty augmentation of 0. +This rule tests that jug `` does not have an empty augmentation of 0. This is a double negative, and although correct in the context of what has been written, it is difficult to understand. An alternative is to test that the ^empty augmentation has a value greater than 0. Soar @@ -1401,7 +1396,7 @@ sp {water-jug*propose*fill Another alternative condition would be to replace the test of empty being greater than zero with a test that the contents don’t equal the -volume: ( ^volume ^contents <> ). +volume: (` ^volume ^contents <> `). One alternative that is not possible is to do some simple math in the conditions to test whether the volume minus the contents equal 0. Soar @@ -1590,7 +1585,7 @@ requires its own operator application rules. We will examine them one at a time. The first is fill. Try to write an English version of its operator application rule. -``` +```Soar water-jug*apply*fill ``` @@ -1628,7 +1623,9 @@ water-jug*propose*fill. The rule to apply the empty operator is essentially the inverse of fill. +```Soar water-jug*apply*empty +``` If the task is water-jug and the empty operator is selected for a given jug, then set that jug’s contents to be 0 and its empty to be its @@ -1664,7 +1661,7 @@ water is left in the source jug, such as when pouring from the full five gallon jug into an empty three gallon jug. As a result, we have to write two rules that cover these two situations. -``` +```Soar water-jug*apply*pour*will-empty-empty-jug ``` @@ -1696,20 +1693,20 @@ sp {water-jug*apply*pour\*will-empty-empty-jug **VisualSoar** – Put this rule, along with the next one, into the file containing the proposal for pour. -This rule shows why it is useful to have the ^empty augmentation on the +This rule shows why it is useful to have the `^empty` augmentation on the jugs. Without it, it would be very difficult to have the test that the jug being filled could hold the water from the jug being poured from. -``` +```Soar water-jug*apply*pour*will-not-empty-empty-jug ``` -If the task is water-jug and the pour operator is selected, and -the contents of the jug being emptied are greater than the empty amount -of the jug being filled, -then set the contents of the jug being emptied to its contents minus the -empty of the jug being filled; -set the contents of the jug filled to its volume. +> If the task is water-jug and the pour operator is selected, and +> the contents of the jug being emptied are greater than the empty amount +> of the jug being filled, +> then set the contents of the jug being emptied to its contents minus the +> empty of the jug being filled; +> set the contents of the jug filled to its volume. ```Soar sp {water-jug*apply*pour*will-not-empty-empty-jug @@ -1744,13 +1741,10 @@ and one for the state). One of the nice things about Soar is that all of the monitoring rules fire in parallel with the other rules and don’t interfere with the problem solving at all. -The write command concatenates all of its arguments, which can include -constants and variables. To make the output readable, you want to have a -linefeed before writing out any text, using the (crlf) command, which -stands for -carriage-return -and -linefeed. +The write command concatenates all of its arguments, which can include constants +and variables. To make the output readable, you want to have a linefeed before +writing out any text, using the (crlf) command, which stands for +`carriage-return` and `line-feed`. ```Soar sp {water-jug\*monitor\*state @@ -1805,8 +1799,8 @@ recognizes when the three-gallon jug has one gallon in it. The action of the rule should be to print out a message that the problem has been solved and halt. Write an English version of this rule. -``` -water-jug*detect*goal\*achieved +```Soar +water-jug*detect*goal*achieved ``` If the task is water-jug and there is a jug with volume three and @@ -1936,7 +1930,7 @@ substructure of that operator will be removed as soon as the rule that created operator retracts. Try to write English versions of these rules. We will show only rules for the pour operator. -``` +```Soar water-jug*apply*operator*record*last-operator*pour ``` @@ -1964,7 +1958,7 @@ The rule to remove old records has to test only if the name of the current operator and the jug augmentation are different because it is not possible to apply an operator twice in a row. -``` +```Soar water-jug*apply*operator*remove*old*last-operator*pour ``` @@ -1992,21 +1986,21 @@ Once you add these rules, add rules that avoid applying an operator that undoes the previous operator (you should write these yourself). -``` +```Soar water-jug*select*operator*avoid*inverse*fill ``` If the task is water-jug and the last operator is empty then avoid a fill. -``` +```Soar water-jug*select*operator*avoid*inverse*empty ``` If the task is water-jug and the last operator is fill then avoid an empty. -``` +```Soar water-jug*select*operator*avoid*inverse*pour ``` diff --git a/docs/tutorials/soar_tutorial/02.md b/docs/tutorials/soar_tutorial/02.md index a3ae4d6e..468067a3 100644 --- a/docs/tutorials/soar_tutorial/02.md +++ b/docs/tutorials/soar_tutorial/02.md @@ -32,7 +32,7 @@ they are teleported to new, random locations on the board. ### Creating an Eater -Start the Eaters environment: +Start the Eaters environment: Open the directory where you installed Eaters and launch the appropriate script file (Eaters.bat on Windows, Eaters.sh on Mac or Linux) @@ -56,7 +56,7 @@ selection box: ![](Images/02/image4.png) -Select the “Agents\\Eaters\\Tutorial” directory with the mouse, and then +Select the “Agents/Eaters/Tutorial” directory with the mouse, and then Choose “Open” (or dbl-click on “Tutorial”). The dialog will display the Soar production files that will be used in this tutorial. Each set of production rules defines specific behaviors for an Eater. Select the @@ -101,8 +101,7 @@ setting Watch levels, and printing information. The layout of the buttons may change depending on the size of the window and user preferences. Features available in the Debugger will be described throughout this document as they are needed. More details are provided -in “Intro to the Soar Debugger in Java.doc” at -. +in [Intro to the Soar Debugger](../IntroSoarDebugger.md). You should also see that the Java Eaters window has changed. The Red Eater is shown on the map, and the Eater name and score are shown in the @@ -175,22 +174,21 @@ the “Clone” button. To create an eater with a different set of rules, press “New” and use the dialog to select the desired rule set just as you did when creating your first move-to-food eater. -![](Images/02/image12.jpeg)From -the Agent List, you can select which eater’s sensor data is displayed by -selecting the eater color with the mouse. When an eater is highlighted, -it can also be “cloned” or destroyed. To destroy an eater, click on the -eater Name in the Agent List (highlight it) and press the “Destroy” -button. - -To reload the production rules for the eater, you should use the reload -button (not implemented yet\!), or if you create an Eater using -VisualSoar you can use the runtime tab to reload the agent, reload a -file, or change a single rule. Reloading is useful if you have found a -bug, modified the rules in the file, and then wish to immediately test -them on the current situation. Soar will replace existing rules with new -versions (a “\#” is printed in the Interaction Window whenever a rule is -replaced), as well as adding any new rules. This can be done at any time -as Soar will match new and modified rules against working memory. +![](Images/02/image12.jpeg) + +From the Agent List, you can select which eater’s sensor data is displayed by +selecting the eater color with the mouse. When an eater is highlighted, it can +also be “cloned” or destroyed. To destroy an eater, click on the eater Name in +the Agent List (highlight it) and press the “Destroy” button. + +To reload the production rules for the eater, you should use the reload button +(not implemented yet\!), or if you create an Eater using VisualSoar you can use +the runtime tab to reload the agent, reload a file, or change a single rule. +Reloading is useful if you have found a bug, modified the rules in the file, and +then wish to immediately test them on the current situation. Soar will replace +existing rules with new versions (a “\#” is printed in the Interaction Window +whenever a rule is replaced), as well as adding any new rules. This can be done +at any time as Soar will match new and modified rules against working memory. ![](Images/02/image13.jpeg) diff --git a/docs/tutorials/soar_tutorial/03.md b/docs/tutorials/soar_tutorial/03.md index c6f43a7f..8181741b 100644 --- a/docs/tutorials/soar_tutorial/03.md +++ b/docs/tutorials/soar_tutorial/03.md @@ -101,25 +101,30 @@ shown for each sensor. sensor will be updated on the input link (“blink”) even if the tank doesn’t move or if the sensor does not otherwise change. -^blocked -^backward yes/no -^forward yes/no -^left yes/no -^right yes/no + ```Soar + ^blocked + ^backward yes/no + ^forward yes/no + ^left yes/no + ^right yes/no + ``` - _Incoming sensor_ The incoming sensor detects whether there is a missile approaching a tank at any distance, unless the missile is on the other side of an obstacle or tank. It does not detect a tank's own missiles. -^incoming -^backward yes/no -^forward yes/no -^left yes/no -^right yes/no + ```Soar + ^incoming + ^backward yes/no + ^forward yes/no + ^left yes/no + ^right yes/no + ``` -- _Radar sensor_ - The radar is functional when a tank has turned the radar on and has +- _Radar sensor_ + + The radar is functional when a tank has turned the radar on and has enough energy to power it. This sensor detects objects in front of the tank in a column that is three squares wide. The distance _d_ which the radar can see is either the current radar setting or the @@ -137,6 +142,8 @@ shown for each sensor. information returned about visible objects is their type and where they were seen (e.g. an obstacle 5 squares in front on the left). If an object is a tank, then its color is also given. + + ```Soar ^radar ^energy ^distance 0-d @@ -157,15 +164,19 @@ shown for each sensor. ^distance 0-d ^position left/center/right ^color red/blue/purple/… + ``` - _Rwaves sensor_ The rwave sensor detects if the radar of another tank is detecting the tank from the four directions. + + ```Soar ^rwaves ^backward yes/no ^forward yes/no ^left yes/no ^right yes/no + ``` - _Smell sensor_ The smell sensors detects the closest tank, and provides information @@ -175,9 +186,12 @@ shown for each sensor. (Manhattan distance). Smell penetrates obstacles, so the smelled tank may not be the tank that is closest to move to. If there are no other tanks, the value of both color and distance will be _none_. + + ```Soar ^smell ^color none/red/blue/purple/… ^distance none/0-28 + ``` - _Sound sensor_ The sound sensor detects the closest tank that moved during the last @@ -191,7 +205,10 @@ shown for each sensor. possible direction, then the sensor chooses one randomly. If there is no tank within 7 squares that moved, the sound sensor will have value _silent_. + + ```Soar ^sound silent/left/right/forward/backward + ``` You can select a tank by clicking on the tank in the Main Map Window. The sensors of the selected tank are shown to the right of the Map. @@ -206,94 +223,138 @@ the input-link and are always functional. - _Clock_ -Clock is initialized to 1 and is incremented each decision. It is a -global counter for all tanks and thus, if a tank is created after others -have run for a while, the initial value will not be 1. -^clock 1-N + Clock is initialized to 1 and is incremented each decision. It is a + global counter for all tanks and thus, if a tank is created after others + have run for a while, the initial value will not be 1. + ^clock 1-N - _Direction_ -The direction that the tank is facing: north, east, south, or west. It -changes when the tank turns. -^direction north/east/south/west + The direction that the tank is facing: north, east, south, or west. It + changes when the tank turns. + + ```Soar + ^direction north/east/south/west + ``` - _Energy_ -The current energy level. If energy is 0, the tank will not be able to -use its radar or shields. -^energy 0-1000 + The current energy level. If energy is 0, the tank will not be able to + use its radar or shields. + + ``` + ^energy 0-1000 + ``` - _Energy recharger_ -If a tank is on an energy recharger (battery), this has value yes. -Otherwise it is no. -^energyrecharger no/yes + If a tank is on an energy recharger (battery), this has value yes. + Otherwise it is no. + + ``` + ^energyrecharger no/yes + ``` - _Health_ -The current health level. If health is 0, the tank dies and is -resurrected at a random square. -^health 0-1000 + The current health level. If health is 0, the tank dies and is + resurrected at a random square. + + ``` + ^health 0-1000 + ``` - _Health recharger_ -If a tank is on a health recharger, this has value yes. Otherwise it is -no. -^healthrecharger no/yes + If a tank is on a health recharger, this has value yes. Otherwise it is no. + + ``` + ^healthrecharger no/yes + ``` - _Missiles_ -The number of missiles the tank currently has. -^missiles 0-N + The number of missiles the tank currently has. + + ``` + ^missiles 0-N + ``` - _My-color_ -The color of the tank. -^my-color blue/red/purple/… + The color of the tank. + + ``` + ^my-color blue/red/purple/… + ``` - _Radar-distance_ -The effective distance of the radar the last time it was used. This is -the distance that the radar reached before it was blocked. If there were -no obstacles, it will be the same as the radar-setting. -^radar-distance 1-14 + The effective distance of the radar the last time it was used. This is + the distance that the radar reached before it was blocked. If there were + no obstacles, it will be the same as the radar-setting. + + ``` + ^radar-distance 1-14 + ``` - _Radar-setting_ -Radar-setting is the distance that the radar has been set to using the -radar-power output command. -^radar-setting 1-14 + Radar-setting is the distance that the radar has been set to using the + radar-power output command. + + ``` + ^radar-setting 1-14 + ``` - _Radar-status_ -Radar-status tells whether the radar is turned on or not. -^radar-status on/off + Radar-status tells whether the radar is turned on or not. + + ``` + ^radar-status on/off + ``` - _Random_ -A random real number between 0 and 1. It changes every decision. -^random 0.0-1.0 + A random real number between 0 and 1. It changes every decision. + + ```Soar + ^random 0.0-1.0 + ``` - _Resurrected_ -Whenever a tank dies and is resurrected, this has value yes. -^resurrected no/yes + Whenever a tank dies and is resurrected, this has value yes. + + ```Soar + ^resurrected no/yes + ``` - _Shield-status_ -Shield-status tells whether the shields are turned on or not. -^shield-status on/off + Shield-status tells whether the shields are turned on or not. + + ```Soar + ^shield-status on/off + ``` - _X_ -X is the x location on the map. X goes from 1 (left side) to 14 (right -side). -^x 1-14 + X is the x location on the map. X goes from 1 (left side) to 14 (right + side). + + ```Soar + ^x 1-14 + ``` - _Y_ -Y is the y location on the map. Y goes from 1 (top) to 14 (bottom). -^y 1-14 + Y is the y location on the map. Y goes from 1 (top) to 14 (bottom). + + ```Soar + ^y 1-14 + ``` ### Tank Actions @@ -306,13 +367,19 @@ except for move and rotate. mutually exclusive with rotating. If a tank tries to move but is blocked, it remains where it is and loses 100 health units. A tank may also move in no direction, which indicates a wait action. + + ```Soar ^move.direction left/right/forward/backward/none + ``` - _Rotate_ A tank can rotate left or right. Rotating is mutually exclusive with moving. A rotate will never fail (assuming the tank is neither killed nor tries to simultaneously move). + + ```Soar ^rotate.direction left/right + ``` - _Fire_ A tank can fire one missile per decision. Firing can be done in @@ -321,54 +388,67 @@ except for move and rotate. decreases a tank's missile supply by one, and it will fail if a tank has no missiles. This failure will be reflected by the tank's missile supply remaining zero. + +§ `Soar ^fire.weapon missile. + ` - _Radar_ A tank can turn its radar on and off. Turning the radar on will fail if a tank does not have enough energy to supply the radar. This failure will be reflected in that ^radar-distance will adjust itself to the highest actually attainable level. + + ```Soar ^radar.switch on/off + ``` - _Radar Range_ -A tank can change the range of its radar by using the radar-power -command. The value can be from 1 to 14. The higher the power setting of -the radar, the more energy is used. -^radar-power.setting 1-14 + A tank can change the range of its radar by using the radar-power + command. The value can be from 1 to 14. The higher the power setting of + the radar, the more energy is used. + + ```Soar + ^radar-power.setting 1-14 + ``` + +- _Shields_ -- _Shields_ - A tank can turn its shields on and off, and this can be done in + A tank can turn its shields on and off, and this can be done in conjunction with any other action. Turning shields on uses 20 energy units per decision. It will fail if a tank does not have enough energy. The failure will be reflected in that ^shield-status will be off. + + ```Soar ^shields.switch on/off + ``` ### Types of Game Objects The following are the objects that can appear on the map: -- ![](Images/03/image5.png)_Obstacles_ +- ![Obstacles](Images/03/image5.png)_Obstacles_ Obstacles look like trees and they are there to get in the way. They never move and they cannot be blown up. There will not be any part of the playing field that is inaccessible purely due to wall placement. (Part of the playing field could be temporarily inaccessible due to a tank blocking the way.) -- ![](Images/03/image6.png)_Healthcharger_ +- ![Healthcharger](Images/03/image6.png)_Healthcharger_ There is one healthcharger per map. The healthcharger never moves. For each decision a tank stays on a healthcharger, its health is increased by 150 minus and any damage it may incur by bumping into something. -- ![](Images/03/image7.png)_Energycharger_ +- ![Energycharger](Images/03/image7.png)_Energycharger_ There is one energycharger (battery) per map. The energycharger never moves. For each decision that a tank stays on an energycharger, its energy is increased by 250 units minus whatever energy it uses for its shields and/or radar. -- ![](Images/03/image8.png)_Packs of +- ![Packs of missiles](Images/03/image8.png)_Packs of missiles_ Packs of missiles are scattered around in random squares. They never move of their own accord nor can they be moved. Missiles flying @@ -377,7 +457,7 @@ The following are the objects that can appear on the map: pick up a pack of missiles by moving to its square. The pack of missiles disappears and the tank's missile supply is increased by 7. -- ![](Images/03/image9.png)_Tanks_ +- ![Tanks](Images/03/image9.png)_Tanks_ Tanks are controlled by TankSoar agents. They do fun stuff like fire missiles and move around, as described above. @@ -392,6 +472,7 @@ missile packs. ## Summary of Input-link and Output-link +```Soar ^io ^input-link ^blocked @@ -460,6 +541,7 @@ forward/backward ^radar.switch on/off ^radar-power.setting 1-14 ^shields.switch on/off +``` ### Tank Combat diff --git a/docs/tutorials/soar_tutorial/07.md b/docs/tutorials/soar_tutorial/07.md index 8188e5e4..39e63c82 100644 --- a/docs/tutorials/soar_tutorial/07.md +++ b/docs/tutorials/soar_tutorial/07.md @@ -5,7 +5,7 @@ deliberately store and retrieve objects that are persistent. This information supplements what is contained in short-term working memory and other long-term memories, such as rules in procedural memory. -### The Semantic Store +## The Semantic Store Before we delve into how an agent can use semantic memory, let’s see an example of preloading knowledge and viewing the contents of the memory. @@ -13,34 +13,35 @@ example of preloading knowledge and viewing the contents of the memory. First, open the Soar Debugger. Then, execute the following command (this can be loaded from a source file just as any other Soar command): +```bash smem --add { ( ^name alice ^friend ) ( ^name bob ^friend ) ( ^name charley) - } - -```` +``` As we shall see in a moment, executing this command adds three objects -to semantic memory. In general, the *smem --add* command is useful to +to semantic memory. In general, the _smem --add_ command is useful to preload the contents of large knowledge bases in Soar. We can view the contents of semantic memory using the following command: +```bash print @ Which will output the following result: (@1 ^friend @2 ^name alice \[+0.000\]) (@2 ^friend @1 ^name bob \[+0.000\]) (@3 ^name charley \[+0.000\]) +``` -Note first that the variables from the *smem --add* command have been -instantiated as specific identifiers ( as @1, as @2, and - as @3). All identifiers in semantic memory are persistent, and -thus we call them *long-term identifiers* (or LTIs), in contrast to all +Note first that the variables from the _smem --add_ command have been +instantiated as specific identifiers (`` as `@1`, `` as `@2`, and +`` as `@3`). All identifiers in semantic memory are persistent, and +thus we call them _long-term identifiers_ (or LTIs), in contrast to all other identifiers, which are short-term. When printed, long-term -identifiers are prefixed by the @ symbol and, when depicted, are shown +identifiers are prefixed by the `@` symbol and, when depicted, are shown using a double circle. The number in square brackets is the bias value of the object, used to break ties during retrievals, a topic to which we shall return later. Finally, unlike working memory and rules, the @@ -51,7 +52,9 @@ To pictorially view the contents of semantic memory, we can use the visualize command to render the contents of semantic memory to an image. For example, execute the following command: +```bash visualize smem +``` If you have graphviz and DOT installed (see for more detail), it should launch your system viewer to show a diagram @@ -64,9 +67,10 @@ of this knowledge is present in any of the agent’s other memories. For instanc execute the following commands to print the contents of working and procedural memories: +```bash print --depth 100 - print +``` You notice that the contents of the semantic store can be completely independent of the other memories, though, as discussed later, an agent @@ -75,7 +79,9 @@ can access and modify the store over time. We are now done with this example and wish to clear the semantic store. To do this we issue a special command: +```bash smem --clear +``` The agent is now reinitialized, as you can verify by printing the contents of working memory, procedural memory, and now semantic memory. @@ -83,12 +89,14 @@ contents of working memory, procedural memory, and now semantic memory. ### Agent Interaction Agents interact with semantic memory via special structures in working -memory. Soar automatically creates an *smem* link on each state, and -each *smem* link has specialized substructure: a *command* link for -agent-initiated actions and a *result* link for feedback from semantic +memory. Soar automatically creates an _smem_ link on each state, and +each _smem_ link has specialized substructure: a _command_ link for +agent-initiated actions and a _result_ link for feedback from semantic memory. For instance, issue the following command: +```bash print --depth 10 +``` If you read the output carefully you will notice a WME that can be generally represented as ( ^smem ) and two additional @@ -96,29 +104,31 @@ WMEs that can be represented as ( ^command ) and ( ^result ). As described in the following sections, the agent, via rules, populates -and maintains the *command* link and the architecture populates and -cleans up the *result* link. +and maintains the _command_ link and the architecture populates and +cleans up the _result_ link. For the agent to interact with semantic memory, this mechanism must be enabled. By default, all learning mechanisms in Soar are disabled. To enable semantic memory, issue the following command: +```bash smem --enable +``` ### Agent Storage and Modification -An agent stores an object to semantic memory by issuing a *store* -command. The syntax of a store command is ( ^store ) where - is the *command* link of a state and is an identifier. +An agent stores an object to semantic memory by issuing a _store_ +command. The syntax of a store command is (` ^store `) where +`` is the _command_ link of a state and `` is an identifier. An agent can issue multiple store commands simultaneously, and the commands are processed at the end of the phase in which they are issued. -A *store* command is guaranteed to succeed and the response from the -architecture will be a success WME: ( ^success ), where -is the *result* link of the state on which the *store* command was -issued and was the value of the *store* command. +A _store_ command is guaranteed to succeed and the response from the +architecture will be a success WME: (` ^success `), where `` +is the _result_ link of the state on which the _store_ command was +issued and `` was the value of the _store_ command. -A *store* command stores the identifier that is the result of the +A _store_ command stores the identifier that is the result of the command, as well as any augmentations of that identifier. The command is not recursive. If the identifier to be stored was not long-term, it is changed in place to a long-term identifier. If it was already in @@ -126,7 +136,7 @@ semantic memory, the augmentations of the long-term identifier in semantic memory are overridden. Let’s see an example. Source the following rules into the Soar Debugger -(they are available in the *smem-tutorial.soar* file within the *Agents* +(they are available in the _smem-tutorial.soar_ file within the _Agents_ directory). ```Soar @@ -137,7 +147,7 @@ sp {propose\*init --> ( ^operator +) ( ^name init)} -```` +``` ```Soar sp {apply\*init @@ -186,47 +196,31 @@ and then the “Run 1 -p” button to watch as the operator is applied. Below is part of the trace that should be produced. If you do not see this part of this trace in your run, be sure that you enabled semantic memory (see section above). + +```bash --- apply phase --- --- Firing Productions (PE) For State At Depth 1 --- - Firing apply\*init - \+ (C3 ^name charley + :O ) (apply\*init) - \+ (B1 ^friend A1 + :O ) (apply\*init) - \+ (B1 ^name bob + :O ) (apply\*init) - \+ (A1 ^friend B1 + :O ) (apply\*init) - \+ (A1 ^name alice + :O ) (apply\*init) - \+ (C2 ^store C3 + :O ) (apply\*init) - \+ (C2 ^store B1 + :O ) (apply\*init) - \+ (C2 ^store A1 + :O ) (apply\*init) - \+ (S1 ^name friends + :O ) (apply\*init) --- Change Working Memory (PE) --- - \=>WM: (25: C3 ^name charley) - \=>WM: (24: B1 ^friend A1) - \=>WM: (23: B1 ^name bob) - \=>WM: (22: A1 ^friend B1) - \=>WM: (21: A1 ^name alice) - \=>WM: (20: C2 ^store A1) - \=>WM: (19: C2 ^store B1) - \=>WM: (18: C2 ^store C3) - \=>WM: (17: S1 ^name friends) +``` Notice that the _apply\*init_ rule fired and added 3 _store_ commands to working memory, where the identifiers to be stored are, initially, not @@ -241,9 +235,10 @@ after using the _smem --add_ command in Part 1. Application of the next operator modifies the contents of semantic memory by overriding the contents of an existing long-term identifier -(@1). Click the “Step” button to select the next operator (_mod_) and +(`@1`). Click the “Step” button to select the next operator (_mod_) and then click the “Run 1 -p" button to apply the operator: +``` Firing apply\*mod - (A1 ^name alice + :O ) (apply\*init) @@ -266,19 +261,23 @@ Firing apply\*mod <=WM: (18: C2 ^store C3) <=WM: (19: C2 ^store B1) +``` -You will notice in the trace that the store commands for @2 and @3 are -removed by the application rule, and that augmentations of @1 are +You will notice in the trace that the store commands for `@2` and `@3` are +removed by the application rule, and that augmentations of `@1` are removed and added. Then, at the end of the elaboration phase, semantic memory cleans up the status information for the old _store_ commands. Now, print the contents of semantic memory using the _print @_ command: + +``` (@1 ^friend @2 @3 ^name anna \[+1.000\]) (@2 ^friend @1 ^name bob \[+1.000\]) (@3 ^name charley \[+1.000\]) +``` -Notice that the augmentations of @1 have indeed changed in semantic -memory to reflect the new _store_ command, while @2 and @3 remain +Notice that the augmentations of `@1` have indeed changed in semantic +memory to reflect the new _store_ command, while `@2` and `@3` remain unchanged. ### Non-Cue-Based Retrieval @@ -331,7 +330,7 @@ sp {apply\*ncb-retrieval\*clean ( ^ -)} ``` -These rules retrieve all the information about one of @1’s two friends +These rules retrieve all the information about one of `@1`’s two friends (selected randomly) and remove the friend’s augmentations (such as name and/or friend) from working memory. @@ -346,16 +345,19 @@ friends, as well as remove that friend’s augmentations from working memory. Then click the “Run 1 -p" button again to proceed through the output phase. Finally, print the full contents of the _smem_ link (_print --depth 10 L1_): + +```bash (L1 ^command C2 ^result R3) (C2 ^depth 3 ^retrieve B1 (@2)) (R3 ^retrieved L2 (@2) ^success B1 (@2)) (L3 ^friend L2 (@2) ^friend L4 (@3) ^name anna) (L2 ^friend L3 (@1) ^name bob) (L4 ^name charley) +``` We see that semantic memory has retrieved and added to working memory the name of the friend, as well as indicated status for this command -(_success_). Your run may have retrieved @3 instead, as a result of the +(_success_). Your run may have retrieved `@3` instead, as a result of the random selection process: Note that had the _retrieve_ command been issued with an identifier that @@ -371,8 +373,8 @@ The second way an agent can retrieve knowledge from semantic memory is called a cue-based retrieval: the agent requests from semantic memory all of the augmentations of an unknown long-term identifier, which is described by a subset of its augmentations. The syntax of the command is -( ^query ), where the desired augmentations all have - as their identifier. +(` ^query `), where the desired augmentations all have +`` as their identifier. The augmentations of the cue form hard constraints, based upon the value of each WME. If the value of the WME is a constant (string, integer, or @@ -424,6 +426,8 @@ application rule create a _query_ command, as well as remove the previous _retrieve_ command from working memory. Then click the “Run 1 -p" button again to proceed through the output phase. Finally print the contents of the _smem_ link (_print --depth 10 L1_): + +``` (L1 ^command C2 ^result R3) (C2 ^depth 3 ^query C4) (C4 ^friend B1 (@2) ^name A2) @@ -431,11 +435,12 @@ contents of the _smem_ link (_print --depth 10 L1_): (L7 ^name charley) (L6 ^friend L5 (@1) ^name bob) (L5 ^friend L6 (@2) ^friend L7 (@3) ^name anna) +``` We see that semantic memory has retrieved and added to working memory -the identifier @1 and all of its augmentations, as well as indicated +the identifier `@1` and all of its augmentations, as well as indicated status for this command (_success_). If in Part 4 of this tutorial your -agent retrieved @3, you may have slightly different output. +agent retrieved `@3`, you may have slightly different output. Note that had no long-term identifier in semantic memory satisfied the constraints of the _query_ command cue, the status would have been