May 10, 2020 Dataset Open Access

Nagashima, Yutaka

MARC21 XML Export

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    <subfield code="a">Isabelle/HOL</subfield>
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    <subfield code="a">Proof Method Recommendation</subfield>
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    <subfield code="a">Machine Learning for Theorem Proving</subfield>
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    <subfield code="a">The corresponding dataset description paper is under review at the 13th Conference on Intelligent Computer Mathematics (CICM2020). The preprint is available at arXiv.org (https://arxiv.org/abs/2004.10667)</subfield>
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    <subfield code="u">Czech Technical University in Prague, University of Innsbruck</subfield>
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    <subfield code="a">Nagashima, Yutaka</subfield>
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    <subfield code="a">Simple Dataset for Proof Method Recommendation in Isabelle/HOL</subfield>
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    <subfield code="a">&lt;p&gt;Recently, a growing number of researchers have applied machine learning to assist users of interactive theorem provers.&lt;/p&gt;

&lt;p&gt;However, the expressive nature of underlying logics and esoteric structures of proof documents impede machine learning practitioners, who often do not have much expertise in formal logic, let alone Isabelle/HOL, from applying their tools and expertise to theorem proving.&lt;/p&gt;

&lt;p&gt;In this data description, we present a simple dataset that contains data on over 400k proof method applications in the Archive of Formal Proofs along with over 100 extracted features for each in a format that can be processed easily without any knowledge about formal logic.&lt;/p&gt;

&lt;p&gt;Our simple data format allows machine learning practitioners to try machine learning tools to predict proof methods in Isabelle/HOL, even if they are unfamiliar with theorem proving.&lt;/p&gt;</subfield>
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