A theory of diagnosis from first principles
Artificial Intelligence
The computational complexity of abduction
Artificial Intelligence - Special issue on knowledge representation
A polynomial-time algorithm for model-based diagnosis
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Characterizing diagnoses and systems
Artificial Intelligence
Building problem solvers
Conflict-directed A* and its role in model-based embedded systems
Discrete Applied Mathematics
A two-step hierarchical algorithm for model-based diagnosis
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Systematic versus stochastic constraint satisfaction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Computing observation vectors for max-fault min-cardinality diagnoses
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
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Most algorithms for computing diagnoses within a modelbased 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 multiplefault 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.