Abstract
Inductive Logic Programming (ILP) is an established sub-field of Machine Learning. Nevertheless, it is recognized that efficiency and scalability is a major obstacle to an increased usage of ILP systems in complex applications with large hypotheses spaces. In this work, we focus on improving the efficiency and scalability of ILP systems by exploring tabling mechanisms available in the underlying Logic Programming systems. Tabling is an implementation technique that improves the declarativeness and performance of Prolog systems by reusing answers to subgoals. To validate our approach, we ran the April ILP system in the YapTab Prolog tabling system using two well-known datasets. The results obtained show quite impressive gains without changing the accuracy and quality of the theories generated.
This work has been partially supported by APRIL (POSI/SRI/40749/2001), Myddas (POSC/EIA/59154/2004), U.S. Air Force (grant F30602-01-2-0571), and by funds granted to LIACC through the Programa de Financiamento Plurianual, Fundação para a Ciência e Tecnologia (FCT) and Programa POSC. Nuno Fonseca is funded by the FCT grant SFRH/BD/7045/2001.
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Rocha, R., Fonseca, N., Costa, V.S. (2005). On Applying Tabling to Inductive Logic Programming. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds) Machine Learning: ECML 2005. ECML 2005. Lecture Notes in Computer Science(), vol 3720. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564096_72
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DOI: https://doi.org/10.1007/11564096_72
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