TITLE: Simple Dataset for Proof Method Recommendation in Isabelle/HOL (Dataset Description) 

ABSTRACT: 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 achieving a large scale success in this field. In
this data description, we present a simple dataset that contains data on over
400k proof method applications 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 without
requiring domain expertise in logic.
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 achieving a large scale success in this field. In
this data description, we present a simple dataset that contains data on over
400k proof method applications 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 without
requiring domain expertise in logic.
Simple Dataset for Proof Method Recommendation in Isabelle/HOL (Dataset Description)
http://arxiv.org/abs/2004.10667v3