Tailoring Local Search for Partial MaxSAT
Author(s)
Cai, Shaowei
Luo, Chuan
Thornton, John
Su, Kaile
Griffith University Author(s)
Year published
2014
Metadata
Show full item recordAbstract
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 ...
View more >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.
View less >
View more >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.
View less >
Conference Title
Proceedings of Twenty-Eighth AAAI Conference on Artificial Intelligence
Subject
Artificial intelligence not elsewhere classified