Towards meta-interpretive learning of programming language semantics
This work addresses a novel application for inductive logic programming, but it is incremental as it builds on existing systems and focuses on a simplified task.
The authors tackled the problem of learning programming language semantics from example evaluations using inductive logic programming, achieving a demonstration with the Metagol system that highlighted challenges like abstracting over function symbols and nonterminating examples.
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.