3819026
doi
10.5281/zenodo.3819026
oai:zenodo.org:3819026
Simple Dataset for Proof Method Recommendation in Isabelle/HOL
Nagashima, Yutaka
Czech Technical University in Prague, University of Innsbruck
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Isabelle/HOL
Proof Method Recommendation
Machine Learning for Theorem Proving
Tactic Recommendation
<p>Recently, a growing number of researchers have applied machine learning to assist users of interactive theorem provers.</p>
<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>
<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>
<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>
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)
Zenodo
2020-05-10
info:eu-repo/semantics/other
3819025
1589401240.788357
126651456
md5:b25be01396b83088e42da80b2e2d10ce
https://zenodo.org/records/3819026/files/Database.txt
public
10.5281/zenodo.3819025
isVersionOf
doi