Computing minimal diagnoses by greedy stochastic search

  • Authors:

  • Alexander Feldman;Gregory Provan;Arjan Van Gemund

  • Affiliations:

  • Delft University of Technology, Delft, The Netherlands;University College Cork, Cork, Ireland;Delft University of Technology, Delft, The Netherlands

  • Venue:
  • AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
  • Year:
  • 2008

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Abstract

Most algorithms for computing diagnoses within a model-based diagnosis framework are deterministic. Such algorithms guarantee soundness and completeness, but are Σ2P-hard. To overcome this complexity problem, which prohibits the computation of high-cardinality diagnoses for large systems, we propose a novel approximation approach for multiple-fault diagnosis, based on a greedy stochastic algorithm called SAFARI (StochAstic Fault diagnosis Algo-RIthm). We prove that SAFARI can be configured to compute diagnoses which are of guaranteed minimality under subsumption. We analytically model SAFARI search as a Markov chain, and show a probabilistic bound on the minimality of its minimal diagnosis approximations. We have applied this algorithm to the 74XXX and ISCAS85 suites of benchmark combinatorial circuits, demonstrating order-of-magnitude speedups over two state-of-the-art deterministic algorithms, CDA* and HA*, for multiple-fault diagnoses.