Towards Finding Longer Proofs
Zsolt Zombori, Adrián Csiszárik, Henryk Michalewski, Cezary Kaliszyk, Josef UrbanAutomated Reasoning with Analytic Tableaux and Related Methods (TABLEAUX 2021), pp. 167-186, 2021.
Abstract
We present a reinforcement learning (RL) based guidance
system for automated theorem proving geared towards Finding Longer
Proofs (FLoP). Unlike most learning based approaches, we focus on
generalising from very little training data and achieving near complete
confidence. We use several simple, structured datasets with very long
proofs to show that FLoP can successfully generalise a single training
proof to a large class of related problems. On these benchmarks, FLoP
is competitive with strong theorem provers despite using very limited
search, due to its ability to solve problems that are prohibitively long for other systems.
BibTeX
@inproceedings{zzachmckju-tableaux21,
author = {Zsolt Zombori and Adrián Csiszárik and Henryk Michalewski and Cezary Kaliszyk and Josef Urban},
editor = {Anupam Das and Sara Negri},
title = {Towards Finding Longer Proofs},
booktitle = {Automated Reasoning with Analytic Tableaux and Related Methods (TABLEAUX 2021)},
series = {LNCS},
volume = {12842},
pages = {167--186},
publisher = {Springer},
year = {2021},
url = {https://doi.org/10.1007/978-3-030-86059-2_10},
doi = {10.1007/978-3-030-86059-2_10},
}