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Approximate Model-Based Diagnosis Using Greedy Stochastic Search

  • Alexander Feldman
  • Gregory Provan
  • Arjan van Gemund
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4612)

Abstract

Most algorithms for computing diagnoses within a model-based diagnosis framework are deterministic. Such algorithms guarantee soundness and completeness, but are NP-hard. To overcome this complexity problem, we propose a novel approximation approach for multiple-fault diagnosis, based on a greedy stochastic algorithm called Safari (StochAstic Fault diagnosis AlgoRIthm). Safari sacrifices guarantees of optimality, but for models in which component failure modes are defined solely in terms of a deviation from nominal behavior (known as weak fault models), it can compute 80-90% of all cardinality-minimal diagnoses, several orders of magnitude faster than state-of-the-art deterministic algorithms. We have applied this algorithm to the 74XXX and ISCAS-85 suites of benchmark combinatorial circuits, demonstrating order-of-magnitude speedup over a well-known deterministic algorithm, CDA*, for multiple-fault diagnoses.

Keywords

Fault Diagnosis Fault Model Consistency Check Deterministic Algorithm Single Fault 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Alexander Feldman
    • 1
  • Gregory Provan
    • 2
  • Arjan van Gemund
    • 1
  1. 1.Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science, Mekelweg 4, 2628 CD, DelftThe Netherlands
  2. 2.University College Cork, Department of Computer Science, College Road, CorkIreland

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