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Learning weak constraints in answer set programming
Published online by Cambridge University Press: 03 September 2015
Abstract
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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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- 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
References
Banbara, M., Soh, T., Tamura, N., Inoue, K. and Schaub, T.
2013. Answer set programming as a modeling language for course timetabling. Theory and Practice of Logic Programming
13, 4–5, 783–798.CrossRefGoogle Scholar
Blockeel, H. and De Raedt, L.
1998. Top-down induction of first-order logical decision trees. Artificial intelligence
101, 1, 285–297.CrossRefGoogle Scholar
Calimeri, F., Faber, W., Gebser, M., Ianni, G., Kaminski, R., Krennwallner, T., Leone, N., Ricca, F. and Schaub, T.
2013. ASP-Core-2 input language format. https://www.mat.unical.it/aspcomp2013/files/ASP-CORE-2.0.pdf.Google Scholar
Corapi, D., Russo, A. and Lupu, E.
2010. Inductive logic programming as abductive search. In ICLP (Technical Communications). 54–63.Google Scholar
Corapi, D., Russo, A. and Lupu, E.
2012. Inductive logic programming in answer set programming. In Inductive Logic Programming. Springer, 91–97.Google Scholar
Dastani, M., Jacobs, N., Jonker, C. M. and Treur, J.
2001. Modeling user preferences and mediating agents in electronic commerce. In Agent Mediated Electronic Commerce. Springer, 163–193.CrossRefGoogle Scholar
Denecker, M., Vennekens, J., Bond, S., Gebser, M. and Truszczyński, M.
2009. The second answer set programming competition. In Logic Programming and Nonmonotonic Reasoning. Springer, 637–654.Google Scholar
Fürnkranz, J. and Hüllermeier, E.
2003. Pairwise preference learning and ranking. In Machine Learning: ECML 2003. Springer, 145–156.CrossRefGoogle Scholar
Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T. and Schneider, M.
2011. Potassco: The Potsdam answer set solving collection. AI Communications
24, 2, 107–124.Google Scholar
Geisler, B., Ha, V. and Haddawy, P.
2001. Modeling user preferences via theory refinement. In Proceedings of the 6th international conference on Intelligent user interfaces. ACM, 87–90.Google Scholar
Horváth, T.
2012. A model of user preference learning for content-based recommender systems. Computing and informatics
28, 4, 453–481.Google Scholar
Kimber, T., Broda, K. and Russo, A.
2009. Induction on failure: learning connected horn theories. In Logic Programming and Nonmonotonic Reasoning. Springer, 169–181.Google Scholar
Lallouet, A., Lopez, M., Martin, L. and Vrain, C.
2010. On learning constraint problems. In Tools with Artificial Intelligence (ICTAI), 2010 22nd IEEE International Conference on. Vol. 1. IEEE, 45–52.Google Scholar
Law, M., Russo, A. and Broda, K.
2014. Inductive learning of answer set programs. In Logics in Artificial Intelligence (JELIA 2014). Springer.Google Scholar
Law, M., Russo, A. and Broda, K.
2015a. The ILASP system for learning answer set programs. https://www.doc.ic.ac.uk/~ml1909/ILASP.Google Scholar
Law, M., Russo, A. and Broda, K.
2015b. Proof of the soundness and completeness of ILASP2. https://www.doc.ic.ac.uk/~ml1909/Proofs_for_ILASP2.pdf.Google Scholar
Law, M., Russo, A. and Broda, K.
2015c. Simplified reduct for choice rules in ASP. Tech. Rep. DTR2015-2, Imperial College of Science, Technology and Medicine, Department of Computing.Google Scholar
Muggleton, S.
1991. Inductive logic programming. New generation computing
8, 4, 295–318.Google Scholar
Muggleton, S., De Raedt, L., Poole, D., Bratko, I., Flach, P., Inoue, K. and Srinivasan, A.
2012. ILP turns 20. Machine Learning
86, 1, 3–23.CrossRefGoogle Scholar
Muggleton, S. and Lin, D.
2013. Meta-interpretive learning of higher-order dyadic datalog: Predicate invention revisited. In Proceedings of the Twenty-Third international joint conference on Artificial Intelligence. AAAI Press, 1551–1557.Google Scholar
Otero, R. P.
2001. Induction of stable models. In Inductive Logic Programming. Springer, 193–205.Google Scholar
Ray, O.
2009. Nonmonotonic abductive inductive learning. Journal of Applied Logic
7, 3, 329–340.Google Scholar
Ray, O., Broda, K. and Russo, A.
2004. A hybrid abductive inductive proof procedure. Logic Journal of IGPL
12, 5, 371–397.Google Scholar
Sakama, C. and Inoue, K.
2009. Brave induction: a logical framework for learning from incomplete information. Machine Learning
76, 1, 3–35.Google Scholar
Srinivasan, A.
2001. The aleph manual. Machine Learning at the Computing Laboratory, Oxford University.Google Scholar
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