Work

Scalable Knowledge Refactoring Using Constrained Optimisation

Year: 2025

Type: conference-paper

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%.

Source: Proceedings of the AAAI Conference on Artificial Intelligence

DOI: 10.1609/aaai.v39i14.33650

Authors: Minghao Liu, David M. Cerna, Filipe Gouveia, Andrew Cropper

Institutions: University of Oxford, Czech Academy of Sciences, Institute of Computer Science

Language: English

Topic: Logic, Reasoning, and Knowledge

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: Engineering and Physical Sciences Research Council

Award: The Automatic Computer Scientist