29 citations · 54 across the 8 of their papers we have counts for
6 papers · 1 filter
The Isabelle ENIGMA
Zarathustra A. Goertzel, Jan Jakubův, Cezary Kaliszyk +3
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 parti…
Adversarial Learning to Reason in an Arbitrary Logic
Stanisław J. Purgał, Cezary Kaliszyk
Existing approaches to learning to prove theorems focus on particular logics and datasets. In this work, we propose Monte-Carlo simulations guided by reinforcement learning that ca…
Can Neural Networks Learn Symbolic Rewriting?
Bartosz Piotrowski, Josef Urban, Chad E. Brown +1
This work investigates if the current neural architectures are adequate for learning symbolic rewriting. Two kinds of data sets are proposed for this research -- one based on autom…
Reinforcement Learning of Theorem Proving
Cezary Kaliszyk, Josef Urban, Henryk Michalewski +1
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 gui…
Learning to Reason with HOL4 tactics
Thibault Gauthier, Cezary Kaliszyk, Josef Urban
Techniques combining machine learning with translation to automated reasoning have recently become an important component of formal proof assistants. Such "hammer" tech- niques com…
HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving
Cezary Kaliszyk, François Chollet, Christian Szegedy
Large computer-understandable proofs consist of millions of intermediate logical steps. The vast majority of such steps originate from manually selected and manually guided heurist…