https://arxiv.org/abs/1904.03241

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Computer Science > Logic in Computer Science

arXiv:1904.03241 (cs)
[Submitted on 5 Apr 2019 (v1), last revised 1 Nov 2019 (this version,
v3)]

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

Authors: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.

Comments: Accepted at ICML 2019
Subjects: Logic in Computer Science (cs.LO); Artificial Intelligence
          (cs.AI); Machine Learning (cs.LG)
Cite as:  arXiv:1904.03241 [cs.LO]
          (or arXiv:1904.03241v3 [cs.LO] for this version)

Submission history

From: Markus N Rabe [view email]
[v1] Fri, 5 Apr 2019 19:04:33 UTC (94 KB)
[v2] Fri, 24 May 2019 00:08:20 UTC (94 KB)
[v3] Fri, 1 Nov 2019 20:16:27 UTC (94 KB)
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Kshitij Bansal
Sarah M. Loos
Markus N. Rabe
Christian Szegedy
Stewart Wilcox
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