Addressing Variable Dependency in GNN-based SAT Solving

• URL: http://arxiv.org/abs/2304.08738v1
• Date: Tue, 18 Apr 2023 05:33:33 GMT
• Title: Addressing Variable Dependency in GNN-based SAT Solving
• Authors: Zhiyuan Yan, Min Li, Zhengyuan Shi, Wenjie Zhang, Yingcong Chen and Hongce Zhang
• Abstract summary: We propose AsymSAT, a GNN-based architecture which integrates recurrent neural networks to generate dependent predictions for variable assignments. 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.
• Score: 19.38746341365531
• License: http://creativecommons.org/licenses/by/4.0/
• 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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