Reinforcement Learning of Theorem Proving [article]

Cezary Kaliszyk, Josef Urban, Henryk Michalewski, Mirek Olšák
2018 arXiv   pre-print
We introduce a theorem proving algorithm that uses practically no domain heuristics for guiding its connection-style proof search. Instead, it runs many Monte-Carlo simulations guided by reinforcement learning from previous proof attempts. We produce several versions of the prover, parameterized by different learning and guiding algorithms. The strongest version of the system is trained on a large corpus of mathematical problems and evaluated on previously unseen problems. The trained system
more » ... ves within the same number of inferences over 40% more problems than a baseline prover, which is an unusually high improvement in this hard AI domain. To our knowledge this is the first time reinforcement learning has been convincingly applied to solving general mathematical problems on a large scale.
arXiv:1805.07563v1 fatcat:c63j46tnmvdh7cposgppuvq4v4