# Towards meta-interpretive learning of programming language semantics

arXiv: https://arxiv.org/abs/1907.08834 · HTML: https://mlsanity.com/paper/1907.08834/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2019.

## Built on by (most cited first, 5 of 5)

- [Inductive logic programming at 30](https://mlsanity.com/paper/2102.10556/) (2021)
- [Learning MDL logic programs from noisy data](https://mlsanity.com/paper/2308.09393/) (2023)
- [Learning logic programs by combining programs](https://mlsanity.com/paper/2206.01614/) (2022)
- [Learning logic programs by discovering where not to search](https://mlsanity.com/paper/2202.09806/) (2022)
- [Learning big logical rules by joining small rules](https://mlsanity.com/paper/2401.16215/) (2024)

## Abstract (arXiv)

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.