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AAAI
2008

Computing Observation Vectors for Max-Fault Min-Cardinality Diagnoses

13 years 6 months ago
Computing Observation Vectors for Max-Fault Min-Cardinality Diagnoses
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 . However, for different 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 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 circ...
Alexander Feldman, Gregory M. Provan, Arjan J. C.
Added 02 Oct 2010
Updated 02 Oct 2010
Type Conference
Year 2008
Where AAAI
Authors Alexander Feldman, Gregory M. Provan, Arjan J. C. van Gemund
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