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On Generating Templates for Hypothesis in Inductive Logic Programming

  • Andrej Chovanec
  • Roman Barták
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7094)

Abstract

Inductive logic programming is a subfield of machine learning that uses first-order logic as a uniform representation for examples and hypothesis. In its core form, it deals with the problem of finding a hypothesis that covers all positive examples and excludes all negative examples. The coverage test and the method to obtain a hypothesis from a given template have been efficiently implemented using constraint satisfaction techniques. In this paper we suggest a method how to efficiently generate the template by remembering a history of generated templates and using this history when adding predicates to a new candidate template. This method significantly outperforms the existing method based on brute-force incremental extension of the template.

Keywords

inductive logic programming template generation constraint satisfaction 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Andrej Chovanec
    • 1
  • Roman Barták
    • 1
  1. 1.Faculty of Mathematics and PhysicsCharles University in PraguePraha 1Czech Republic

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