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On Applying Tabling to Inductive Logic Programming

Rocha, Ricardo ; Fonseca, Nuno ; Costa, Vítor Santos

Lecture notes in computer science, 2005, p.707-714 [Periódico revisado por pares]

Berlin, Heidelberg: Springer Berlin Heidelberg

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  • Título:
    On Applying Tabling to Inductive Logic Programming
  • Autor: Rocha, Ricardo ; Fonseca, Nuno ; Costa, Vítor Santos
  • Assuntos: Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Exact sciences and technology ; Learning and adaptive systems
  • É parte de: Lecture notes in computer science, 2005, p.707-714
  • Notas: 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.
  • Descrição: 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.
  • Títulos relacionados: Lecture Notes in Computer Science
  • Editor: Berlin, Heidelberg: Springer Berlin Heidelberg
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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