Graph Neural Networks and Boolean Satisfiability

Abstract

In this paper we explore whether or not deep neural architectures can learn to classify Boolean satisfiability (SAT). We devote considerable time to discussing the theoretical properties of SAT. Then, we define a graph representation for Boolean formulas in conjunctive normal form, and train neural classifiers over general graph structures called Graph Neural Networks, or GNNs, to recognize features of satisfiability. To the best of our knowledge this has never been tried before. Our preliminary findings are potentially profound. In a weakly-supervised setting, that is, without problem specific feature engineering, Graph Neural Networks can learn features of satisfiability.

Cited by in corpus (6)

• A Review of Machine Learning Applications in Fuzzing
• Machine Learning Methods in Solving the Boolean Satisfiability Problem
• Improving Graph Attention Networks with Large Margin-based Constraints
• MEUZZ: Smart Seed Scheduling for Hybrid Fuzzing
• Automated proof synthesis for propositional logic with deep neural networks
• Making Logic Learnable With Neural Networks