Abstract
Inductive logic programming (ILP) [12] is concerned with the induction of theories from specific examples and background knowledge, using first-order logic representations for all the three ingredients. In its early days some twenty years ago, ILP was perceived as a means for automatic synthesis of logic programs, i.e. Horn clausal theories. Current research views ILP algorithms mainly in the context of machine learning [14] and data mining [1]. ILP has enriched both of the two fieds significantly by providing them with formalisms and algorithms for learning (or ‘mining’) complex pieces of knowledge from non-trivially structured data such as relational databases.
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References
Džeroski, S., Lavrač, N. (eds.): Relational Data Mining. Springer, Heidelberg (2001)
Gomes, C., Selman, B., Crato, N., Kautz, H.: Heavy-Tailed Phenomena in Satisfiability and Constraint Satisfaction Problems. Journal of Automated Reasoning 24, 67–100 (2000)
Gottlob, G.: Subsumption and Implication. Information Processing Letters 24, 109–111 (1987)
Gottlob, G., Leone, N., Scarcello, F.: On the Complexity of Some Inductive Logic Programming Problems. New Generation Computing 17, 53–75 (1999)
Kautz, H., Horvitz, E., Ruan, Y., Gomes, C., Selman, B.: Dynamic Restart Policies. In: AAAI 2002, Association for the Advancement of Artificial Intelligence Symposium (2002)
Kirkpatrick, S., Selman, B.: Critical Behavior in the Satisfiability of Random Boolean Expressions. Science 264, 1297–1301 (1994)
Kuželka, O., Železný, F.: Block-Wise Construction of Acyclic Relational Features with Monotone Irreducibility and Relevancy Properties. In: ICML 2009, the 26th Int. Conf. on Machine Learning (2009)
Kuželka, O., Železný, F.: Fast Estimation of First-Order Clause Coverage through Randomization and Maximum Likelihood. In: ICML 2008, the 25th Int. Conf. on Machine Learning (2008)
Kuželka, O., Železný, F.: A Restarted Strategy for Efficient Subsumption Testing. Fundamenta Informaticae 89, 95–109 (2008)
Maloberti, J., Sebag, M.: Fast Theta-Subsumption with Constraint Satisfaction Algorithms. Machine Learning 55(2), 137–174 (2004)
Marcinkowski, J., Pacholski, L.: Undecidability of the Horn-Clause Implication Problem. In: FOCS 2002, the 33rd Ann. Sympos. on Foundations of Computer Science (2002)
Nienhuys-Cheng, S.H., de Wolf, R.: Foundations of Inductive Logic Programming. Springer, Heidelberg (1997)
Raedt, L.D.: Logical Settings for Concept Learning. Artificial Intelligence 95, 187–201 (1997)
Raedt, L.D.: Logical and Relational Learning. Springer, Heidelberg (2008)
Raedt, L.D., Frasconi, P., Kersting, K., Muggleton, S. (eds.): Probabilistic Inductive Logic Programming. Springer, Heidelberg (2008)
Železný, F., Lavrač, N.: Propositionalization-Based Relational Subgroup Discovery with RSD. Machine Learning 62, 33–63 (2006)
Železný, F., Srinivasan, A., Page, D.: Lattice Search Runtime Distributions May Be Heavy Tailed. In: Matwin, S., Sammut, C. (eds.) ILP 2002. LNCS (LNAI), vol. 2583, pp. 333–345. Springer, Heidelberg (2003)
Železný, F., Srinivasan, A., Page, D.: Randomized Restarted Search in ILP. Machine Learning 64, 183–208 (2006)
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Železný, F., Kuželka, O. (2010). Taming the Complexity of Inductive Logic Programming. In: van Leeuwen, J., Muscholl, A., Peleg, D., Pokorný, J., Rumpe, B. (eds) SOFSEM 2010: Theory and Practice of Computer Science. SOFSEM 2010. Lecture Notes in Computer Science, vol 5901. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11266-9_11
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