Learning weak constraints in answer set programming
File(s)
Accepted version
Author(s)
Law, M
Russo, A
Broda, K
Type
Journal Article
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.
Date Issued
2015-09-03
Date Acceptance
2015-07-15
Citation
Theory and Practice of Logic Programming, 2015, 15 (4-5), pp.511-525
ISSN
1475-3081
Publisher
Cambridge University Press
Start Page
511
End Page
525
Journal / Book Title
Theory and Practice of Logic Programming
Volume
15
Issue
4-5
Copyright Statement
© 2015 Cambridge University Press. This paper has been accepted for publication and will appear in a revised form, subsequent to peer-review and/or editorial input by Cambridge University Press.
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Grant Number
EP/K033425/1
Subjects
Science & Technology
Technology
Computer Science, Software Engineering
Computer Science, Theory & Methods
Logic
Computer Science
Science & Technology - Other Topics
Non-monotonic Inductive Logic Programming
Preference Learning
Answer Set Programming
INDUCTION
Computation Theory & Mathematics
0803 Computer Software
0801 Artificial Intelligence And Image Processing
Publication Status
Published