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Model-based diagnosis with improved implicit hitting set dualization
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DOI：10.1007/s10489-021-02408-0.png)
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Abstract
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Model-based Diagnosis (MBD) with multiple observations is a currently complicated problem with many applications and solving this problem is attracting more and more attention. This paper propose an improved algorithm, called Improved implicit Hitting Set Dualization (IHSD), which is the integration of gate domination in recent works for computing cardinality-minimal aggregated diagnoses in MBD problems. First, our approach works by separating components into dominated components and non-dominated components according to structure of diagnosis system. The separated components are modelled as hard clauses and soft clauses separately. Additionally, two feasible approaches, called IHSDa and IHSDb, are proposed to expand one cardinality-minimal aggregated diagnosis to more diagnoses. Experimental results on 74XXX and ISCAS85 benchmarks clearly show that IHSD algorithm improves HSD, DC and DC*. Moreover, IHSDa and IHSDb outperform HSD on solving more diagnoses.
Keywords:
Model-based diagnosis
Maximum Satisfiability
Top-level diagnosis
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