Addressing Variable Dependency in GNN-based SAT Solving

Zhiyuan Yan; Min Li; Zhengyuan Shi; Wenjie Zhang; Yingcong Chen; Hongce Zhang

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arXiv · 2304.08738

Addressing Variable Dependency in GNN-based SAT Solving

Zhiyuan Yan·Min Li·Zhengyuan Shi·Wenjie Zhang·Yingcong Chen·Hongce Zhang

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Abstract

Boolean satisfiability problem (SAT) is fundamental to many applications. Existing works have used graph neural networks (GNNs) for (approximate) SAT solving. Typical GNN-based end-to-end SAT solvers predict SAT solutions concurrently. We show that for a group of symmetric SAT problems, the concurrent prediction is guaranteed to produce a wrong answer because it neglects the dependency among Boolean variables in SAT problems. % We propose AsymSAT, a GNN-based architecture which integrates recurrent neural networks to generate dependent predictions for variable assignments. The experiment results show that dependent variable prediction extends the solving capability of the GNN-based method as it improves the number of solved SAT instances on large test sets.

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Zhiyuan Yan, Min Li, Zhengyuan Shi, Wenjie Zhang, Yingcong Chen, Hongce Zhang. 2023-04-18. Addressing Variable Dependency in GNN-based SAT Solving. https://arxiv.org/abs/2304.08738

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