Scalable Knowledge Refactoring using Constrained Optimisation
This addresses scalability issues in knowledge refactoring for logic programming, which is incremental but offers practical improvements.
The paper tackles the problem of scaling knowledge refactoring for large logic programs by introducing a constrained optimisation approach with decision variables based on literals and linear invented rules, achieving up to 60% faster refactoring and better compression compared to the previous state-of-the-art.
Knowledge refactoring compresses a logic program by introducing 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%.