Solving strong-fault diagnostic models by model relaxation

  • Authors:

  • Alexander Feldman;Gregory Provan;Arjan Van Gemund

  • Affiliations:

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

  • Venue:
  • IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
  • Year:
  • 2009

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Abstract

In Model-Based Diagnosis (MBD), the problem of computing a diagnosis in a strong-fault model (SFM) is computationally much harder than in a weak-fault model (WFM). For example, in propositional Horn models, computing the first minimal diagnosis in a weak-fault model (WFM) is in P but is NP-hard for strong-fault models. As a result, SFM problems of practical significance have not been studied in great depth within the MBD community. In this paper we describe an algorithm that renders the problem of computing a diagnosis in several important SFM subclasses no harder than a similar computation in a WFM. We propose an approach for efficiently computing minimal diagnoses for these subclasses of SFM that extends existing conflict-based algorithms like GDE (Sherlock) and CDA*. Experiments on ISCAS85 combinational circuits show (1) inference speedups with CDA*of up to a factor of 8, and (2) an average of 28% reduction in the average conflict size, at the price of an extra low-polynomial-time consistency check for a candidate diagnosis.