Guiding High-Performance SAT Solvers with Unsat-Core Predictions
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The NeuroSAT neural network architecture was recently introduced for predicting properties of propositional formulae. When trained to predict the satisfiability of toy problems, it was shown to find solutions and unsatisfiable cores on its own. However, the authors saw "no obvious path" to using the architecture to improve the state-of-the-art. In this work, we train a simplified NeuroSAT architecture to directly predict the unsatisfiable cores of real problems. We modify several state-of-the-art SAT solvers to periodically replace their variable activity scores with NeuroSAT's prediction of how likely the variables are to appear in an unsatisfiable core. The modified MiniSat solves 10 5,000 second timeout than the original does. The modified Glucose solves 11 more problems than the original, while the modified Z3 solves 6 results demonstrate that NeuroSAT can provide effective guidance to high-performance SAT solvers on real problems.
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