safeprocess15-ensembles.tex

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\begin{document}
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\begin{frontmatter}
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\title{Learning Diagnosis Models Using Variable-Fidelity Component Model Libraries\thanksref{footnoteinfo}}
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\thanks[footnoteinfo]{Supported by SFI grant 12/RC/2289.}
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\author[First]{Alexander Feldman}
\author[Second]{Gregory Provan}
\author[First]{Rui Abreu}
\author[First]{Johan de Kleer}
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\address[First]{PARC Inc., Palo Alto, CA 94304, USA\\(e-mail: \{afeldman,dekleer,rui\}@parc.com)}
\address[Second]{Department of Computer Science, University College Cork, Cork, Ireland (e-mail: g.provan@cs.ucc.ie).}
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\begin{abstract}
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System models that are used in model-based diagnosis are often
composed of components drawn from component libraries. In these component
libraries, there may be multiple systems of equations per component
(component implementations). For example, a component may be modeled
as a non-linear system (high-fidelity model), linear system, and a
qualitative system (low-fidelity model). Choosing the right component
model for system diagnosis is a difficult task and requires a search
in the space of all possible component type combinations. In this
paper we propose a method that automates this task and computes a
system model that optimizes a set of diagnostic metrics in a set of
diagnostic scenarios. Initial experimental results show that having
linear models of some of the components in a system preserves the
diagnostic accuracy and isolation time while, at the same time,
improves the computational complexity and numerical stability.
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\end{abstract}
\end{frontmatter}
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\bibliography{safeprocess15-ensembles}
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\end{document}