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sinatav
reviewed
Nov 5, 2021
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| "cell_type": "markdown", | |||
| "metadata": {}, | |||
sinatav
reviewed
Nov 5, 2021
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| "cell_type": "markdown", | |||
| "metadata": {}, | |||
| "source": [ | |||
| "# Informed Search\n", | |||
sinatav
reviewed
Nov 5, 2021
| "\n", | ||
| "Informed search is an approach to solving problems with a start state to a goal state, that is based on some mathematical concepts that estimate our distance to a goal state and inform us about some of following states. \n", |
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and rewriting an already written notebook made this reviewing process really hard for me, so thx :)))
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can you explain this comment?
did I screw something up with GitHub?
apologies in advance.
sinatav
reviewed
Nov 5, 2021
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "" | ||
| "## A* Search\n", |
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make stronger connections between different parts of your notebook, don't jump from one topic to another make a suitable setup and a solid structure
sinatav
reviewed
Nov 5, 2021
| "\n", | ||
| "• f(G_2)>f(G) from above\n", | ||
| "In the left diagram, the heuristic’s estimated cost value always (in all states) remains below the true cost value so h is admissible, however, in the right diagram, h sometimes overestimates the real cost value and violates admissibility condition.\n", | ||
| "The second and important condition on heuristic function, is Monotonicity or also called consistency. A heuristic function h(n) is monotonic, is consistent if, for every node n and every successor of n generated by any action a, the estimated cost of reaching the goal from n is no greater than the step cost of getting to plus the estimated cost of reaching the goal from n':\n", |
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review your grammar (for example the second and important)
sinatav
reviewed
Nov 5, 2021
| "Where n = nodes the A* expands. It is obvious that if n is too big for main memory, n2 is too long to wait. To address this issue, we need to either change our heuristic function of A* algorithm steps. But there is another way! Another algorithm called SMA* or Simplified memory bounded A* is developed to circumvent the memory problem. In this algorithm, we define how much memory we have, and therefore how many nodes should be expanded. SMA* stores all expanded nodes and open the nodes in memory, and if the memory is full it deletes the leaf with highest f value and backs up the value in its parent. You can find an example of this algorithm in the pictures below:\n", | ||
| "\n", | ||
| "\n", | ||
| "" | ||
| ] |
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write a brief conclusion or a summary as your outro :))
sinatav
reviewed
Nov 5, 2021
| "Where n = nodes the A* expands. It is obvious that if n is too big for main memory, n2 is too long to wait. To address this issue, we need to either change our heuristic function of A* algorithm steps. But there is another way! Another algorithm called SMA* or Simplified memory bounded A* is developed to circumvent the memory problem. In this algorithm, we define how much memory we have, and therefore how many nodes should be expanded. SMA* stores all expanded nodes and open the nodes in memory, and if the memory is full it deletes the leaf with highest f value and backs up the value in its parent. You can find an example of this algorithm in the pictures below:\n", | ||
| "\n", | ||
| "\n", | ||
| "" | ||
| ] | ||
| }, |
sinatav
reviewed
Nov 5, 2021
| @@ -4,514 +4,281 @@ | |||
| "cell_type": "markdown", | |||
sinatav
reviewed
Nov 5, 2021
| "Where n = nodes the A* expands. It is obvious that if n is too big for main memory, n2 is too long to wait. To address this issue, we need to either change our heuristic function of A* algorithm steps. But there is another way! Another algorithm called SMA* or Simplified memory bounded A* is developed to circumvent the memory problem. In this algorithm, we define how much memory we have, and therefore how many nodes should be expanded. SMA* stores all expanded nodes and open the nodes in memory, and if the memory is full it deletes the leaf with highest f value and backs up the value in its parent. You can find an example of this algorithm in the pictures below:\n", | ||
| "\n", | ||
| "\n", | ||
| "" | ||
| ] | ||
| }, |
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Also a "other useful links" section can be helpful
|
Edited and uploaded index.md |
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