Formulating the template ILP consistency problem as a constraint satisfaction problem
- 151 Downloads
Inductive Logic Programming (ILP) deals with the problem of finding a hypothesis covering positive examples and excluding negative examples, where both hypotheses and examples are expressed in first-order logic. In this paper we employ constraint satisfaction techniques to model and solve a problem known as template ILP consistency, which assumes that the structure of a hypothesis is known and the task is to find unification of the contained variables. In particular, we present a constraint model with index variables accompanied by a Boolean model to strengthen inference and hence improve efficiency. The efficiency of models is demonstrated experimentally.
KeywordsConstraint modeling Inductive logic programming Meta-reasoning
Unable to display preview. Download preview PDF.
- 1.Alphonse, E., & Osmani, A. (2009). Empirical study of relational learning algorithms in the phase transition framework. In Machine learning and knowledge discovery in databases (pp. 51–66).Google Scholar
- 2.Baptiste, P., Le Pape, C., Nuijten, W. (2001). Constraint-based scheduling: Applying constraint programming to scheduling problems. Kluwer Academic Publishers.Google Scholar
- 4.Barták, R. (2010). Constraint models for reasoning on unification in inductive logic programming. In Artificial Intelligence: Methodology, Systems, and Applications (AIMSA 2010) (pp. 101–110). Springer Verlag.Google Scholar
- 5.Barták, R., Kuželka,O., Železný, F. (2010). Using constraint satisfaction for learning hypotheses in inductive logic programming. In Proceedings of the 23rd international Florida AI Research Society conference (FLAIRS 2010) (pp. 440–441). AAAI Press.Google Scholar
- 6.Bordeaux, L., & Monfroy, E. (2002). Beyond NP: Arc-Consistency for quantified constraints. In Principles and practice of Constraint Programming—CP 2002 (pp. 17–32). Springer Verlag.Google Scholar
- 7.Botta, M. Challenging relational learning—dipartimento di informatica—università di torino. http://www.di.unito.it/~mluser/challenge/index.html. Accessed 6 February 2013.
- 8.Carlsson, M., & Beldiceanu, N. (2002). Arc-Consistency for a chain of lexicographic ordering constraints. http://soda.swedish-ict.se/2267. Accessed 6 February 2013.
- 9.Chovanec, A., & Barták, R. (2011). On generating templates for hypothesis in inductive logic programming. In Advances in artificial intelligence (proceedings of 10th Mexican International Conference on Artificial Intelligence (MICAI 2011), Part 1 (pp. 162–173). Springer VerlagGoogle Scholar
- 10.Dechter, R. (2003). Constraint processing. Morgan Kaufmann Publishers Inc.Google Scholar
- 11.Džeroski, S., & Lavrač, N. (2001). Relational data mining. Springer Verlag.Google Scholar
- 12.Erdős, P., & Rényi, A. (1959). On the evolution of random graphs. Publicationes Mathematicae, 6, 290—297.Google Scholar
- 13.Garey, M.R., & Johnson, D.S. (1979). Computers and intractability: A guide to the theory of NP-Completeness. W. H. Freeman & Co.Google Scholar
- 14.Giunchiglia, F., & Sebastiani, R. (1996). Building decision procedures for modal logics from propositional decision procedures—the case study of modal K(m). In CADE13: Proceedings of 13th international conference on automated deduction (pp. 583–597). Springer Verlag.Google Scholar
- 17.Landwehr, N., Kersting, K., De Raedt, L. (2005). nFOIL: Integrating naive bayes and FOIL. In Proceedings of the 20th national conference on Artificial intelligence—Volume 2 (pp. 795–800). AAAI Press.Google Scholar
- 20.Plotkin, G., Meltzer, B., Michie, D. (1970). A note on inductive generalization. Machine Intelligence, 5, 153–163.Google Scholar
- 21.Sabin, D., & Freuder, E.C. (1994). Contradicting conventional wisdom in constraint satisfaction. Principles and practice of constraint programming (pp. 162–173). Springer Verlag.Google Scholar
- 22.Srinivasan, A. Aleph manual. http://www.comlab.ox.ac.uk/activities/machinelearning/Aleph. Accessed 6 February 2013.