Automated theorem provers and formal proof assistants are general reasoning systems that
are in theory capable of proving arbitrarily hard theorems, thus solving arbitrary problems …

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 …

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 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 present a system that utilizes machine learning for tactic proof search in the Coq Proof
Assistant. In a similar vein as the TacticToe project for HOL4, our system predicts …

As a present to Mizar on its 50th anniversary, we develop an AI/TP system that automatically
proves about 60\% of the Mizar theorems in the hammer setting. We also automatically …

We describe several additions to the ENIGMA system that guides clause selection in the E
automated theorem prover. First, we significantly speed up its neural guidance by adding …

In this work in progress, we demonstrate a new use-case for the ENIGMA system. The
ENIGMA system using the XGBoost implementation of gradient boosted decision trees has …

Reinforcement learning (RL)[18], especially its deep variant relying on modern neural
networks, is probably the most fashionable method for attacking problems in our machine …

With artificial intelligence (AI) being well established within the daily lives of research
communities, we turn our gaze toward an application area that appears intuitively unsuited …