Artificial Intelligence
Characterizing diagnoses and systems
Artificial Intelligence
The complexity of logic-based abduction
Journal of the ACM (JACM)
Implementing the Davis–Putnam Method
Journal of Automated Reasoning
Unveiling the ISCAS-85 Benchmarks: A Case Study in Reverse Engineering
IEEE Design & Test
Multiple-Fault Simulation and Coverage of Deterministic Single-Fault Test Sets
Proceedings of the IEEE International Test Conference on Test: Faster, Better, Sooner
A maximal resolution guided-probe testing algorithm
DAC '81 Proceedings of the 18th Design Automation Conference
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
Automated benchmark model generators for model-based diagnostic inference
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Approximate model-based diagnosis using greedy stochastic search
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
Test Generation for Model-Based Diagnosis
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Computing minimal diagnoses by greedy stochastic search
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
A benchmark diagnostic model generation system
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on model-based diagnostics
Approximate model-based diagnosis using greedy stochastic search
Journal of Artificial Intelligence Research
A model-based active testing approach to sequential diagnosis
Journal of Artificial Intelligence Research
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Model-Based Diagnosis (MBD) typically focuses on diagnoses, minimal under some minimality criterion, e.g., the minimal-cardinality set of faulty components that explain an observation α. However, for different α there may be minimal-cardinality diagnoses of differing cardinalities, and several applications (such as test pattern generation and benchmark model analysis) need to identify the α leading to the max-cardinality diagnosis amongst them. We denote this problem as a Max-Fault Min-Cardinality (MFMC) problem. This paper considers the generation of observations that lead to MFMC diagnoses. We present a near-optimal, stochastic algorithm, called MIRANDA (Max-fault mIn-caRdinAlity observatioN Deduction Algorithm), that computes MFMC observations. Compared to optimal, deterministic approaches such as ATPG, the algorithm has very low cost, allowing us to generate observations corresponding to high-cardinality faults. Experiments show that MIRANDA delivers optimal results on the 74XXX circuits, as well as good MFMC cardinality estimates on the larger ISCAS85 circuits.