GraSS: Combining Graph Neural Networks with Expert Knowledge for SAT Solver Selection

• URL: http://arxiv.org/abs/2405.11024v1
• Date: Fri, 17 May 2024 18:00:50 GMT
• Title: GraSS: Combining Graph Neural Networks with Expert Knowledge for SAT Solver Selection
• Authors: Zhanguang Zhang, Didier Chetelat, Joseph Cotnareanu, Amur Ghose, Wenyi Xiao, Hui-Ling Zhen, Yingxue Zhang, Jianye Hao, Mark Coates, Mingxuan Yuan,
• Abstract summary: We present GraSS, a novel approach for automatic SAT solver selection based on tripartite graph representations of instances. We enrich the graph representation with domain-specific decisions, such as novel node feature design. We demonstrate that this combination of raw representations and domain-specific choices leads to improvements in runtime.
• Score: 45.222591894755105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Boolean satisfiability (SAT) problems are routinely solved by SAT solvers in real-life applications, yet solving time can vary drastically between solvers for the same instance. This has motivated research into machine learning models that can predict, for a given SAT instance, which solver to select among several options. Existing SAT solver selection methods all rely on some hand-picked instance features, which are costly to compute and ignore the structural information in SAT graphs. In this paper we present GraSS, a novel approach for automatic SAT solver selection based on tripartite graph representations of instances and a heterogeneous graph neural network (GNN) model. While GNNs have been previously adopted in other SAT-related tasks, they do not incorporate any domain-specific knowledge and ignore the runtime variation introduced by different clause orders. We enrich the graph representation with domain-specific decisions, such as novel node feature design, positional encodings for clauses in the graph, a GNN architecture tailored to our tripartite graphs and a runtime-sensitive loss function. Through extensive experiments, we demonstrate that this combination of raw representations and domain-specific choices leads to improvements in runtime for a pool of seven state-of-the-art solvers on both an industrial circuit design benchmark, and on instances from the 20-year Anniversary Track of the 2022 SAT Competition.

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