Learning weak constraints in answer set programming

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 witho...

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Veröffentlicht in:Theory and practice of logic programming Jg. 15; H. 4-5; S. 511 - 525
Hauptverfasser: LAW, MARK, RUSSO, ALESSANDRA, BRODA, KRYSIA
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cambridge, UK Cambridge University Press 01.07.2015
Schlagworte:
Algorithms
Learning
Logic programming
Programming
Regular Papers
Sound
Task scheduling
Tasks
Preference Learning
Answer Set Programming
Non-monotonic Inductive Logic Programming
ISSN:1471-0684, 1475-3081
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Zusammenfassung: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.
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ISSN:1471-0684
1475-3081
DOI:10.1017/S1471068415000198