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Domain Dependent Parameter Setting in SAT Solver Using Machine Learning Techniques

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Agents and Artificial Intelligence (ICAART 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13786))

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Abstract

We address the problem of variable and truth-value choice in modern search-based Boolean satisfiability (SAT) solvers depending on the problem domain. The SAT problem is the task to determine truth-value assignment for variables of a given Boolean formula under which the formula evaluates to true. The SAT problem is often used as a canonical representation of combinatorial problems in many domains of computer science ranging from artificial intelligence to software engineering. Modern complete search-based SAT solvers represent a universal problem solving tool which often provide higher efficiency than ad-hoc direct solving approaches. Many efficient variable and truth-value selection heuristics were devised. Heuristics can usually be fine tuned by single or multiple numerical parameters prior to executing the search process over the concrete SAT instance. In this paper we present a machine learning approach that predicts the parameters of heuristic from the underlying structure of a graph derived from the input SAT instance. Using this approach we effectively fine tune the SAT solver for specific problem domain.

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Acknowledgements

This research has been supported by GAČR - the Czech Science Foundation, grant registration number 22-31346S.

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Authors and Affiliations

  1. Faculty of Information Technology, Czech Technical University, Thákurova 9, 160 00, Praha 6, Czech Republic

    Filip Beskyd & Pavel Surynek

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  1. Filip Beskyd
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  2. Pavel Surynek
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Correspondence to Pavel Surynek .

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Editors and Affiliations

  1. LIACC/FEUP, Porto, Portugal

    Ana Paula Rocha

  2. ICREA, Institute of Evolutionary Biology, Barcelona, Spain

    Luc Steels

  3. Leiden Institute of Advanced Computer Science, Leiden, The Netherlands

    Jaap van den Herik

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Beskyd, F., Surynek, P. (2022). Domain Dependent Parameter Setting in SAT Solver Using Machine Learning Techniques. In: Rocha, A.P., Steels, L., van den Herik, J. (eds) Agents and Artificial Intelligence. ICAART 2022. Lecture Notes in Computer Science(), vol 13786. Springer, Cham. https://doi.org/10.1007/978-3-031-22953-4_8

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  • DOI: https://doi.org/10.1007/978-3-031-22953-4_8

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