Learning Weak Constraints in Answer Set Programming
This work addresses the problem of learning preferences and constraints in ASP for applications like interview scheduling, but it is incremental as it builds on prior frameworks.
The paper introduces a new learning framework for inductive logic programming that learns weak constraints in Answer Set Programming (ASP), generalizing prior work by using ordered pairs of partial answer sets as examples to capture preferences. It presents the ILASP2 algorithm, which is sound and complete, and shows it can be more efficient than previous systems when learning ASP programs without weak constraints.
This paper contributes to the area of inductive logic programming by presenting a new learning framework that allows the learning of weak constraints in Answer Set Programming (ASP). The framework, called Learning from Ordered Answer Sets, generalises our previous work on learning ASP programs without weak constraints, by considering a new notion of examples as ordered pairs of partial answer sets that exemplify which answer sets of a learned hypothesis (together with a given background knowledge) are preferred to others. In this new learning task inductive solutions are searched within a hypothesis space of normal rules, choice rules, and hard and weak constraints. We propose a new algorithm, ILASP2, which is sound and complete with respect to our new learning framework. We investigate its applicability to learning preferences in an interview scheduling problem and also demonstrate that when restricted to the task of learning ASP programs without weak constraints, ILASP2 can be much more efficient than our previously proposed system.