AAAI 2023technical5 citations
Learning Logic Programs by Discovering Where Not to Search
Andrew Cropper, Céline Hocquette
Abstract
The goal of inductive logic programming (ILP) is to search for a hypothesis that generalises training examples and background knowledge (BK). To improve performance, we introduce an approach that, before searching for a hypothesis, first discovers "where not to search". We use given BK to discover constraints on hypotheses, such as that a number cannot be both even and odd. We use the constraints to bootstrap a constraint-driven ILP system. Our experiments on multiple domains (including program synthesis and inductive general game playing) show that our approach can (i) substantially reduce learning times by up to 97%, and (ii) can scale to domains with millions of facts.
BibTeX
@article{Cropper_Hocquette_2023, title={Learning Logic Programs by Discovering Where Not to Search}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/25774}, DOI={10.1609/aaai.v37i5.25774}, abstractNote={The goal of inductive logic programming (ILP) is to search for a hypothesis that generalises training examples and background knowledge (BK). To improve performance, we introduce an approach that, before searching for a hypothesis, first discovers "where not to search". We use given BK to discover constraints on hypotheses, such as that a number cannot be both even and odd. We use the constraints to bootstrap a constraint-driven ILP system. Our experiments on multiple domains (including program synthesis and inductive general game playing) show that our approach can (i) substantially reduce learning times by up to 97%, and (ii) can scale to domains with millions of facts.}, number={5}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Cropper, Andrew and Hocquette, Céline}, year={2023}, month={Jun.}, pages={6289-6296} }