English

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