Abstract
Propositional satisfiability (SAT) is a prominent problem in artificial intelligence with many important applications. Stochastic local search (SLS) is a well-known approach for solving SAT and known to achieve excellent performance on randomly generated, satisfiable instances. However, SLS solvers for SAT are usually ineffective in solving application instances. Here, we propose a highly configurable SLS solver dubbed PbO-CCSAT, which leverages a powerful technique known as configuration checking (CC) in combination with the automatic algorithm design paradigm of programming by optimisation (PbO). Our PbO-CCSAT solver exposes a large number of design choices, which are automatically configured to optimise the performance for specific classes of SAT instances. We present extensive empirical results showing that our PbO-CCSAT solver significantly outperforms state-of-the-art SLS solvers on SAT instances from many applications, and further show that PbO-CCSAT is complementary to state-of-the-art complete solvers.
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Luo, C., Hoos, H., Cai, S. (2020). PbO-CCSAT: Boosting Local Search for Satisfiability Using Programming by Optimisation. In: Bäck, T., et al. Parallel Problem Solving from Nature – PPSN XVI. PPSN 2020. Lecture Notes in Computer Science(), vol 12269. Springer, Cham. https://doi.org/10.1007/978-3-030-58112-1_26
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DOI: https://doi.org/10.1007/978-3-030-58112-1_26
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