Transformers satisfy
The Propositional Satisfiability Problem (SAT), and more generally, the Constraint Satisfaction Problem (CSP), are mathematical questions defined as finding an assignment to a set of objects that satisfies a series of constraints. The modern approach is trending to solve CSP through neural symbolic methods. Most recent works are sequential model-based and adopt neural embedding, i.e., reinforcement learning with neural graph networks, and graph recurrent neural networks. This work proposes a one-shot model derived from the eminent Transformer architecture for factor graph structure to solve the CSP problem. We define the heterogeneous attention mechanism based on meta-paths for the self-attention between literals, the cross-attention based on the bipartite graph links from literal to clauses, or vice versa. This model takes advantage of parallelism. Our model achieves high speed and very high accuracy on the factor graph for CSPs with arbitrary size.
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