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.
CITATION STYLE
Nagashima, Y. (2020). Simple dataset for proof method recommendation in isabelle/hol. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12236 LNAI, pp. 297–302). Springer. https://doi.org/10.1007/978-3-030-53518-6_21
Mendeley helps you to discover research relevant for your work.