# Towards meta-interpretive learning of programming language semantics

## TL;DR

This paper explores using meta-interpretive learning to automatically infer programming language semantics from examples, addressing challenges like abstraction and non-termination with proposed system extensions.

## Contribution

It introduces a novel application of inductive logic programming to learn programming language semantics and proposes extensions to the Metagol system to handle domain-specific challenges.

## Key findings

- Identified key challenges in learning language semantics from examples
- Proposed system extensions to Metagol for better handling of non-termination and abstraction
- Demonstrated potential of meta-interpretive learning in this domain

## 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.