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...

• Type: Conference
• Year: 2008
• Where: AAAI
• Authors: Alexander Feldman, Gregory M. Provan, Arjan J. C. van Gemund
• Keywords: AAAI 2008, Intelligent Agents, Max-fault Min-cardinality, Observation Deduction Algorithm, Test Pattern Generation