Artificial Intelligence
A theory of diagnosis from first principles
Artificial Intelligence
Artificial Intelligence
Using crude probability estimates to guide diagnosis
Artificial Intelligence
The computational complexity of abduction
Artificial Intelligence - Special issue on knowledge representation
Symbolic Boolean manipulation with ordered binary-decision diagrams
ACM Computing Surveys (CSUR)
“Physical Negation”—integrating fault models into the General Diagnostic Engine
Readings in model-based diagnosis
Characterizing diagnoses and systems
Artificial Intelligence
Building problem solvers
A linear constraint satisfaction approach to cost-based abduction
Artificial Intelligence
Cost-based abduction and MAP explanation
Artificial Intelligence
The complexity of logic-based abduction
Journal of the ACM (JACM)
Improvements to propositional satisfiability search algorithms
Improvements to propositional satisfiability search algorithms
On the hardness of approximate reasoning
Artificial Intelligence
A machine program for theorem-proving
Communications of the ACM
Unveiling the ISCAS-85 Benchmarks: A Case Study in Reverse Engineering
IEEE Design & Test
The Impact of Branching Heuristics in Propositional Satisfiability Algorithms
EPIA '99 Proceedings of the 9th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
SAT-Encodings, Search Space Structure, and Local Search Performance
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Counting Models Using Connected Components
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Scaling and Probabilistic Smoothing: Efficient Dynamic Local Search for SAT
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
The complexity of theorem-proving procedures
STOC '71 Proceedings of the third annual ACM symposium on Theory of computing
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Design diagnosis using Boolean satisfiability
Proceedings of the 2004 Asia and South Pacific Design Automation Conference
Approximating cost-based abduction is NP-hard
Artificial Intelligence
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
Computing minimal diagnoses by greedy stochastic search
AAAI'08 Proceedings of the 23rd national 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
Model-based diagnosis using structured system descriptions
Journal of Artificial Intelligence Research
Counting complexity of propositional abduction
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
From sampling to model counting
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Systematic versus stochastic constraint satisfaction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Physical impossibility instead of fault models
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Efficient conflict analysis for finding all satisfying assignments of a boolean circuit
TACAS'05 Proceedings of the 11th international conference on Tools and Algorithms for the Construction and Analysis of Systems
Fault diagnosis and logic debugging using Boolean satisfiability
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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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 ISCAS85 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.