Dataset Open Access

Simple Dataset for Proof Method Recommendation in Isabelle/HOL

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": "", 
  "creator": [
      "affiliation": "Czech Technical University in Prague, University of Innsbruck", 
      "@id": "", 
      "@type": "Person", 
      "name": "Nagashima, Yutaka"
  "url": "", 
  "datePublished": "2020-05-10", 
  "keywords": [
    "Proof Method Recommendation", 
    "Machine Learning for Theorem Proving", 
    "Tactic Recommendation"
  "@context": "", 
  "distribution": [
      "contentUrl": "", 
      "encodingFormat": "txt", 
      "@type": "DataDownload"
  "identifier": "", 
  "@id": "", 
  "@type": "Dataset", 
  "name": "Simple Dataset for Proof Method Recommendation in Isabelle/HOL"
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