May 10, 2020 Dataset Open Access

Nagashima, Yutaka

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    "description": "<p>Recently, a growing number of researchers have applied machine learning to assist users of interactive theorem provers.</p>\n\n<p>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.</p>\n\n<p>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.</p>\n\n<p>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.</p>", 
    "license": {
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    "title": "Simple Dataset for Proof Method Recommendation in Isabelle/HOL", 
    "notes": "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)", 
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    "keywords": [
      "Isabelle/HOL", 
      "Proof Method Recommendation", 
      "Machine Learning for Theorem Proving", 
      "Tactic Recommendation"
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    "publication_date": "2020-05-10", 
    "creators": [
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        "affiliation": "Czech Technical University in Prague, University of Innsbruck", 
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