Abstract
Knowledge refactoring compresses logic programs by replacing them with new rules. Current approaches struggle to scale to large programs. To overcome this limitation, we introduce a constrained optimisation refactoring approach. Our first key idea is to encode the problem with decision variables based on literals rather than rules. Our second key idea is to focus on linear invented rules. Our empirical results on multiple domains show that our approach can refactor programs quicker and with more compression than the previous state-of-the-art approach, sometimes by 60%.
Type
Publication
In Proceedings of the AAAI Conference on Artificial Intelligence
Filipe Gouveia
Computer Science Researcher
My research interests include artificial intelligence, computational logic and automated reasoning.