Parameter Setting in SAT Solver using Machine Learning Techniques

Filip Beskyd; Pavel Surynek

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Paper

Parameter Setting in SAT Solver using Machine Learning Techniques

Topics: Constraint Satisfaction; Machine Learning; State Space Search

In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, 586-597, 2022

Authors: Filip Beskyd and Pavel Surynek

Affiliation: Faculty of Information Technology, Czech Technical University, Thákurova 9, 160 00 Praha 6, Czech Republic

Keyword(s): SAT Problem, Boolean Satisfiability, Solver, Graph Structure, Machine Learning, Heuristic Parameter Tuning.

Abstract: Boolean satisfiability (SAT) solvers are essential tools for many domains in computer science and engineering. Modern complete search-based SAT solvers represent a universal problem solving tool which often provide higher efficiency than ad-hoc direct solving approaches. Over the course of at least two decades of SAT related research, many variable and value selection heuristics were devised. Heuristics can usually be 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 the input SAT instance.

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Paper citation in several formats:

Beskyd, F. and Surynek, P. (2022). Parameter Setting in SAT Solver using Machine Learning Techniques. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 586-597. DOI: 10.5220/0010910200003116

@conference{icaart22,
author={Filip Beskyd. and Pavel Surynek.},
title={Parameter Setting in SAT Solver using Machine Learning Techniques},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2022},
pages={586-597},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010910200003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Parameter Setting in SAT Solver using Machine Learning Techniques
SN - 978-989-758-547-0
IS - 2184-433X
AU - Beskyd, F.
AU - Surynek, P.
PY - 2022
SP - 586
EP - 597
DO - 10.5220/0010910200003116
PB - SciTePress