Learning Weak Constraints in Answer Set Programming

TL;DR

This paper introduces a new framework and algorithm for learning weak constraints in Answer Set Programming, enabling the modeling of preferences and improving efficiency in certain learning tasks.

Contribution

It presents a novel learning framework for weak constraints in ASP and an algorithm, ILASP2, that is sound, complete, and more efficient for specific cases.

Findings

ILASP2 effectively learns preferences in scheduling problems.

The framework generalizes previous work by incorporating weak constraints.

ILASP2 outperforms previous systems in learning ASP without weak constraints.

Abstract

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.