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Learning weak constraints in answer set programming

Published online by Cambridge University Press:  03 September 2015

MARK LAW ,
ALESSANDRA RUSSO  and
KRYSIA BRODA
MARK LAW
Affiliation:
Department of Computing, Imperial College London, SW7 2AZ (e-mail: [email protected], [email protected], [email protected])
ALESSANDRA RUSSO
Affiliation:
Department of Computing, Imperial College London, SW7 2AZ (e-mail: [email protected], [email protected], [email protected])
KRYSIA BRODA
Affiliation:
Department of Computing, Imperial College London, SW7 2AZ (e-mail: [email protected], [email protected], [email protected])
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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.

Keywords

Non-monotonic Inductive Logic ProgrammingPreference LearningAnswer Set Programming
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 15 , Special Issue 4-5: 31st International Conference on Logic Programming , July 2015 , pp. 511 - 525
Copyright
Copyright © Cambridge University Press 2015 

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