Dataset Open Access

Simple Dataset for Proof Method Recommendation in Isabelle/HOL

Nagashima, Yutaka

Recently, a growing number of researchers have applied machine learning to assist users of interactive theorem provers.

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.

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.

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.

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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