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arXiv · 1401.3848

Approximate Model-Based Diagnosis Using Greedy Stochastic Search

Alexander Feldman·Gregory Provan·Arjan van Gemund
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Abstract

We propose a StochAstic Fault diagnosis AlgoRIthm, called SAFARI, which trades off guarantees of computing minimal diagnoses for computational efficiency. We empirically demonstrate, using the 74XXX and ISCAS-85 suites of benchmark combinatorial circuits, that SAFARI achieves several orders-of-magnitude speedup over two well-known deterministic algorithms, CDA* and HA*, for multiple-fault diagnoses; further, SAFARI can compute a range of multiple-fault diagnoses that CDA* and HA* cannot. We also prove that SAFARI is optimal for a range of propositional fault models, such as the widely-used weak-fault models (models with ignorance of abnormal behavior). We discuss the optimality of SAFARI in a class of strong-fault circuit models with stuck-at failure modes. By modeling the algorithm itself as a Markov chain, we provide exact bounds on the minimality of the diagnosis computed. SAFARI also displays strong anytime behavior, and will return a diagnosis after any non-trivial inference time.

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Alexander Feldman, Gregory Provan, Arjan van Gemund. 2014-01-16. Approximate Model-Based Diagnosis Using Greedy Stochastic Search. https://doi.org/10.1613/jair.3025

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