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 …

Automated reasoning and theorem proving have recently become major challenges for
machine learning. In other domains, representations that are able to abstract over …

We propose a novel approach to interactive theorem-proving (ITP) using deep reinforcement
learning. The proposed framework is able to learn proof search strategies as well as tactic …

We describe several datasets and first experiments with creating conjectures by neural
methods. The datasets are based on the Mizar Mathematical Library processed in several …

We present a reinforcement learning toolkit for experiments with guiding automated theorem
proving in the connection calculus. The core of the toolkit is a compact and easy to extend …

We describe a very large improvement of existing hammer-style proof automation over large
ITP libraries by combining learning and theorem proving. In particular, we have integrated …

We significantly improve the performance of the E automated theorem prover on the Isabelle
Sledgehammer problems by combining learning and theorem proving in several ways. In …

We re-examine the topic of machine-learned clause selection guidance in saturation-based
theorem provers. The central idea, recently popularized by the ENIGMA system, is to learn a …

We present Tactician, a tactic learner and prover for the Coq Proof Assistant. Tactician helps
users make tactical proof decisions while they retain control over the general proof strategy …

State-of-the-art automated theorem provers explore large search spaces with carefully-
engineered routines, but most do not learn from past experience as human mathematicians …