May 10, 2020 Dataset Open Access

Nagashima, Yutaka

DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="DOI">10.5281/zenodo.3819026</identifier>
  <creators>
    <creator>
      <creatorName>Nagashima, Yutaka</creatorName>
      <givenName>Yutaka</givenName>
      <familyName>Nagashima</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-6693-5325</nameIdentifier>
      <affiliation>Czech Technical University in Prague, University of Innsbruck</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Simple Dataset for Proof Method Recommendation in Isabelle/HOL</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>Isabelle/HOL</subject>
    <subject>Proof Method Recommendation</subject>
    <subject>Machine Learning for Theorem Proving</subject>
    <subject>Tactic Recommendation</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-05-10</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3819026</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3819025</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&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;</description>
    <description descriptionType="Other">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)</description>
  </descriptions>
</resource>