W2SAT: Learning to generate SAT instances from Weighted Literal Incidence Graphs

• URL: http://arxiv.org/abs/2302.00272v2
• Date: Tue, 24 Sep 2024 06:38:20 GMT
• Title: W2SAT: Learning to generate SAT instances from Weighted Literal Incidence Graphs
• Authors: Weihuang Wen, Tianshu Yu,
• Abstract summary: W2SAT is a framework to generate SAT formulas by learning intrinsic structures and properties from given real-world/industrial instances. We introduce a novel SAT representation called Weighted Literal Incidence Graph (WLIG), which exhibits strong representation ability and generalizability. Decoding from WLIG into SAT problems is then modeled as finding overlapping cliques with a novel hill-climbing optimization method.
• Score: 11.139131079925113
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Boolean Satisfiability (SAT) problem stands out as an attractive NP-complete problem in theoretic computer science and plays a central role in a broad spectrum of computing-related applications. Exploiting and tuning SAT solvers under numerous scenarios require massive high-quality industry-level SAT instances, which unfortunately are quite limited in the real world. To address the data insufficiency issue, in this paper, we propose W2SAT, a framework to generate SAT formulas by learning intrinsic structures and properties from given real-world/industrial instances in an implicit fashion. To this end, we introduce a novel SAT representation called Weighted Literal Incidence Graph (WLIG), which exhibits strong representation ability and generalizability against existing counterparts, and can be efficiently generated via a specialized learning-based graph generative model. Decoding from WLIGs into SAT problems is then modeled as finding overlapping cliques with a novel hill-climbing optimization method termed Optimal Weight Coverage (OWC). Experiments demonstrate the superiority of our WLIG-induced approach in terms of graph metrics, efficiency, and scalability in comparison to previous methods. Additionally, we discuss the limitations of graph-based SAT generation for real-world applications, especially when utilizing generated instances for SAT solver parameter-tuning, and pose some potential directions.

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