Dataset Open Access

Simple Dataset for Proof Method Recommendation in Isabelle/HOL

Nagashima, Yutaka


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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.3819026", 
  "title": "Simple Dataset for Proof Method Recommendation in Isabelle/HOL", 
  "issued": {
    "date-parts": [
      [
        2020, 
        5, 
        10
      ]
    ]
  }, 
  "abstract": "<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>", 
  "author": [
    {
      "family": "Nagashima, Yutaka"
    }
  ], 
  "note": "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)", 
  "type": "dataset", 
  "id": "3819026"
}
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