Neural heuristics for SAT solving

We use neural graph networks with a message-passing architecture and an attention mechanism to enhance the branching heuristic in two SATsolving algorithms. We report improvements of learned neural heuristics compared with two standard human-designed heuristics. We compare the performance in terms of number of branching decisions and show the possibility of enhancing the performance of SAT solvers with the help of learned heuristics. A similar graph representation, but more general in order to accommodate for higher-order logic is used in FormulaNet presented in [WTWD17]. To the best of our knowledge the FormulaNet architecture was never used for neural guidance.