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Model-based diagnosis with improved implicit hitting set dualization

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Abstract

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.

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Acknowledgements

This work was supported by the National Natural Science Foundation China (62076108, 61872159, 61672261).

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Authors and Affiliations

  1. Laboratory of Symbolic Computation and Knowledge Engineering, College of Computer Science and Technology, Jilin University, Changchun, China

    Huisi Zhou, Dantong Ouyang, Liming Zhang & Naiyu Tian

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  1. Huisi Zhou
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  2. Dantong Ouyang
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  3. Liming Zhang
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  4. Naiyu Tian
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Correspondence to Liming Zhang.

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Zhou, H., Ouyang, D., Zhang, L. et al. Model-based diagnosis with improved implicit hitting set dualization. Appl Intell 52, 2111–2118 (2022). https://doi.org/10.1007/s10489-021-02408-0

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