Towards meta-interpretive learning of programming language semantics
Programming Languages
2019-07-23 v1 Machine Learning
Logic in Computer Science
Abstract
We introduce a new application for inductive logic programming: learning the semantics of programming languages from example evaluations. In this short paper, we explored a simplified task in this domain using the Metagol meta-interpretive learning system. We highlighted the challenging aspects of this scenario, including abstracting over function symbols, nonterminating examples, and learning non-observed predicates, and proposed extensions to Metagol helpful for overcoming these challenges, which may prove useful in other domains.
Cite
@article{arxiv.1907.08834,
title = {Towards meta-interpretive learning of programming language semantics},
author = {Sándor Bartha and James Cheney},
journal= {arXiv preprint arXiv:1907.08834},
year = {2019}
}
Comments
ILP 2019, to appear