ENIGMA Anonymous: Symbol-Independent Inference Guiding Machine (System Description)

Jan Jakubuv, Karel Chvalovský, Miroslav Olsák, Bartosz Piotrowski, Martin Suda, Josef Urban

Abstract: We describe an implementation of gradient boosting and neural guidance of saturation-style automated theorem provers that does not depend on consistent symbol names across problems. For the gradient-boosting guidance, we manually create abstracted features by considering arity-based encodings of formulas. For the neural guidance, we use symbol-independent graph neural networks (GNNs) and their embedding of the terms and clauses. The two methods are efficiently implemented in the E prover and its ENIGMA learning-guided framework.