Approximate Model-Based Diagnosis Using Greedy Stochastic Search
  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.
