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arXiv · 2307.09141

Machine Learning for SAT: Restricted Heuristics and New Graph Representations

Mikhail Shirokikh·Ilya Shenbin·Anton Alekseev·Sergey Nikolenko
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Abstract

Boolean satisfiability (SAT) is a fundamental NP-complete problem with many applications, including automated planning and scheduling. To solve large instances, SAT solvers have to rely on heuristics, e.g., choosing a branching variable in DPLL and CDCL solvers. Such heuristics can be improved with machine learning (ML) models; they can reduce the number of steps but usually hinder the running time because useful models are relatively large and slow. We suggest the strategy of making a few initial steps with a trained ML model and then releasing control to classical heuristics; this simplifies cold start for SAT solving and can decrease both the number of steps and overall runtime, but requires a separate decision of when to release control to the solver. Moreover, we introduce a modification of Graph-Q-SAT tailored to SAT problems converted from other domains, e.g., open shop scheduling problems. We validate the feasibility of our approach with random and industrial SAT problems.

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Mikhail Shirokikh, Ilya Shenbin, Anton Alekseev, Sergey Nikolenko. 2023-07-18. Machine Learning for SAT: Restricted Heuristics and New Graph Representations. https://arxiv.org/abs/2307.09141

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