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arXiv · 1904.03241

HOList: An Environment for Machine Learning of Higher-Order Theorem Proving

Kshitij Bansal·Sarah M. Loos·Markus N. Rabe·Christian Szegedy·Stewart Wilcox
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Abstract

We present an environment, benchmark, and deep learning driven automated theorem prover for higher-order logic. Higher-order interactive theorem provers enable the formalization of arbitrary mathematical theories and thereby present an interesting, open-ended challenge for deep learning. We provide an open-source framework based on the HOL Light theorem prover that can be used as a reinforcement learning environment. HOL Light comes with a broad coverage of basic mathematical theorems on calculus and the formal proof of the Kepler conjecture, from which we derive a challenging benchmark for automated reasoning. We also present a deep reinforcement learning driven automated theorem prover, DeepHOL, with strong initial results on this benchmark.

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Kshitij Bansal, Sarah M. Loos, Markus N. Rabe, Christian Szegedy, Stewart Wilcox. 2019-11-01. HOList: An Environment for Machine Learning of Higher-Order Theorem Proving. https://arxiv.org/abs/1904.03241

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