NeuroDual: A Hybrid SAT Solver Combining Graph Attention Networks with Algorithmic Techniques

Mahfuza Farooque, Matthew Walter, Aaron Falk

Abstract: In this paper, we introduce NeuroDual, a hybrid Boolean satisfiability (SAT) solver architecture that integrates Graph Attention Networks (GATs) into the Conflict-Driven Clause Learning (CDCL) process. Unlike traditional SAT solvers with fixed decision heuristics, NeuroDual leverages GATs to dynamically learn a decision heuristic specific to each SAT instance by computing an assignment score for each variable in the problem. Also, GATs in the context of SAT-solving allow NeuroDual to better understand the spatial relations of the dynamic features within a CNF clause and make informed predictions for variable assignments in the SAT instance that align more closely with the current state of the problem. For our CDCL component of NeuroDual, we implement MINISAT as the baseline solver. Our results show that incorporating machine learning techniques, specifically GATs, into SAT-solving algorithms as a decision heuristic has the potential to increase solver efficiency. These findings indicate that integrating machine learning techniques with traditional SAT-solving algorithms like CDCL to enhance the decision heuristic has the potential to drive efficiency improvements, paving the way for smarter decision-making, reduced conflict occurrences, and expedited problem resolution.

External IDs:dblp:conf/isncc/FarooqueWF24