Computing Minimal Diagnoses by Greedy Stochastic Search

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.