A Deep Reinforcement Learning Approach to First-Order Logic Theorem Proving
Maxwell Crouse, Ibrahim Abdelaziz, Bassem Makni, Spencer Whitehead, Cristina Cornelio, Pavan Kapanipathi, Kavitha Srinivas, Veronika Thost
Abstract
Automated theorem provers have traditionally relied on manually tuned heuristics to guide how they perform proof search. Deep reinforcement learning has been proposed as a way to obviate the need for such heuristics, however, its deployment in automated theorem proving remains a challenge. In this paper we introduce TRAIL, a system that applies deep reinforcement learning to saturation-based theorem proving. TRAIL leverages (a) a novel neural representation of the state of a theorem prover and (b) a novel characterization of the inference selection process in terms of an attention-based action policy. We show through systematic analysis that these mechanisms allow TRAIL to significantly outperform previous reinforcement-learning-based theorem provers on two benchmark datasets for first-order logic automated theorem proving (proving around 15% more theorems).
BibTeX
@inproceedings{aaai2021_adeepreinforceme,
title = {A Deep Reinforcement Learning Approach to First-Order Logic Theorem Proving},
author = {Maxwell Crouse and Ibrahim Abdelaziz and Bassem Makni and Spencer Whitehead and Cristina Cornelio and Pavan Kapanipathi and Kavitha Srinivas and Veronika Thost and Michael Witbrock and Achille Fokoue},
booktitle = {AAAI 2021},
year = {2021}
}