HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving
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Proceedings of the 36th International Conference on Machine Learning, PMLR 97:454463, 2019.
Abstract
We present an environment, benchmark, and deep learning driven automated theorem prover for higherorder logic. Higherorder interactive theorem provers enable the formalization of arbitrary mathematical theories and thereby present an interesting challenge for deep learning. We provide an opensource 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 approaches. We also present a deep reinforcement learning driven automated theorem prover, DeepHOL, that gives strong initial results on this benchmark.
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