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  • Tailoring Local Search for Partial MaxSAT

    Author(s)
    Cai, Shaowei
    Luo, Chuan
    Thornton, John
    Su, Kaile
    Griffith University Author(s)
    Su, Kaile
    Year published
    2014
    Metadata
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    Abstract
    Partial MaxSAT (PMS) is a generalization to SAT and MaxSAT. Many real world problems can be encoded into PMS in a more natural and compact way than SAT and MaxSAT. In this paper, we propose new ideas for local search for PMS, which mainly rely on the distinction between hard and soft clauses. We use these ideas to develop a local search PMS algorithm called {/it Dist}. Experimental results on PMS benchmarks from MaxSAT Evaluation 2013 show that {/it Dist} significantly outperforms state-of-the-art PMS algorithms, including both local search algorithms and complete ones, on random and crafted benchmarks. For the industrial ...
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    Partial MaxSAT (PMS) is a generalization to SAT and MaxSAT. Many real world problems can be encoded into PMS in a more natural and compact way than SAT and MaxSAT. In this paper, we propose new ideas for local search for PMS, which mainly rely on the distinction between hard and soft clauses. We use these ideas to develop a local search PMS algorithm called {/it Dist}. Experimental results on PMS benchmarks from MaxSAT Evaluation 2013 show that {/it Dist} significantly outperforms state-of-the-art PMS algorithms, including both local search algorithms and complete ones, on random and crafted benchmarks. For the industrial benchmark, {/it Dist} dramatically outperforms previous local search algorithms and is comparable with complete algorithms.
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    Conference Title
    Proceedings of Twenty-Eighth AAAI Conference on Artificial Intelligence
    Publisher URI
    https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8141
    Subject
    Artificial intelligence not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/68370
    Collection
    • Conference outputs

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