Holophrasm: a neural Automated Theorem Prover for higher-order logic
Artificial Intelligence
2016-08-11 v2 Logic in Computer Science
Abstract
I propose a system for Automated Theorem Proving in higher order logic using deep learning and eschewing hand-constructed features. Holophrasm exploits the formalism of the Metamath language and explores partial proof trees using a neural-network-augmented bandit algorithm and a sequence-to-sequence model for action enumeration. The system proves 14% of its test theorems from Metamath's set.mm module.
Keywords
Cite
@article{arxiv.1608.02644,
title = {Holophrasm: a neural Automated Theorem Prover for higher-order logic},
author = {Daniel Whalen},
journal= {arXiv preprint arXiv:1608.02644},
year = {2016}
}
Comments
9 pages, 1 figure