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Jiang Luyu, Ouyang Dantong, Zhang Qi, Tai Ran, Zhang Liming. Incremental Information Interaction-Based Method for Enumerating MUSes[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202440326
Citation: Jiang Luyu, Ouyang Dantong, Zhang Qi, Tai Ran, Zhang Liming. Incremental Information Interaction-Based Method for Enumerating MUSes[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202440326

Incremental Information Interaction-Based Method for Enumerating MUSes

Funds: This work was supported by the National Natural Science Foundation of China (62076108, 61872159) and the Scientific Research Project of Education Department of Jilin Province (JJKH20211106KJ, JJKH20211103KJ).
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  • Author Bio:

    Jiang Luyu: born in 1998. PhD candidate. Student member of CCF. Her main research interests include SAT, combinatorial optimization, and model-based diagnosis problem

    Ouyang Dantong: born in 1968. PhD, professor, PhD supervisor. Senior member of CCF. Her main research interests include automatic reasoning and model-based diagnosis problem. (ouyd@jlu.edu.cn

    Zhang Qi: born in 1998. PhD candidate. Student member of CCF. His main research interests include SAT and model-based diagnosis problem

    Tai Ran: born in 1997. PhD candidate. Student member of CCF. His main research interests include combinatorial optimization and model-based diagnosis problem

    Zhang Liming: born in 1980. PhD, senior engineer. Senior member of CCF. His main research interests include SAT and model-based diagnosis problem

  • Received Date: May 20, 2024
  • Revised Date: September 28, 2024
  • Accepted Date: October 14, 2024
  • Available Online: October 21, 2024
  • Enumerating minimal unsatisfiable subsets (MUS) is an important issue in theoretical computer science. Given an unsatisfiable Boolean formula, the number of its MUS is exponentially related to the formula’s scale. Contemporary methods aim to identify as many MUSes as possible within appropriate time limits. Dealing with the huge search space, choosing a suitable node to expand can markedly reduce the time consumption on shrink and grow operations, thereby the algorithm could obtain better performance. This paper introduces an incremental information interaction-based MUS solving method, denoted as MARCO-MSS4MUS, which utilizes the duality and complementary relationships among MUS, minimal correction sets (MCS), and maximal satisfiable subsets (MSS). Based on the framework of MARCO algorithm, the proposed method selects a more suitable node to expand via intersection and union information of previously identified MSSes during the search, i.e., the incremental MSS information is employed as a heuristic for node selection to accelerate the enumeration of MUS. This process also benefits in identifying more MSSes, in turn, the incremental MSS information help select a better node for next exploration, thereby achieving an interaction of incremental information. The paper presents two theorems and two corollaries regarding interactive incremental information, analyzing the feasibility of our MARCO-MSS4MUS algorithm theoretically. Experiments on standard MUS benchmark instances show the superiority of the proposed algorithm over state-of-the-art methods. Both the enumeration efficiency and the number of enumerated wins of the proposed method are significantly improved compared to existing methods.

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