Conflict Generalisation in ASP: Learning Correct and Effective Non-Ground Constraints

TL;DR

This paper introduces a method to generalize learned non-ground constraints in ASP, combining logic-based machine learning with traditional solving techniques to improve efficiency across multiple problem instances.

Contribution

It presents a novel approach to re-using learned constraints in ASP solving, reducing computational costs and enhancing performance through conflict minimization.

Findings

Learned constraints significantly improve solving speed.

Low computational cost for learning effective constraints.

Conflict minimization reduces grounding and solving efforts.

Abstract

Generalising and re-using knowledge learned while solving one problem instance has been neglected by state-of-the-art answer set solvers. We suggest a new approach that generalises learned nogoods for re-use to speed-up the solving of future problem instances. Our solution combines well-known ASP solving techniques with deductive logic-based machine learning. Solving performance can be improved by adding learned non-ground constraints to the original program. We demonstrate the effects of our method by means of realistic examples, showing that our approach requires low computational cost to learn constraints that yield significant performance benefits in our test cases. These benefits can be seen with ground-and-solve systems as well as lazy-grounding systems. However, ground-and-solve systems suffer from additional grounding overheads, induced by the additional constraints in some cases. By means of conflict minimization, non-minimal learned constraints can be reduced. This can result in significant reductions of grounding and solving efforts, as our experiments show. (Under consideration for acceptance in TPLP.)