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Using Abduction to Learn Horn Theories

  • D. Gunetti
Conference paper
Part of the International Centre for Mechanical Sciences book series (CISM, volume 363)

Abstract

A method for learning Horn theories based on a systematic use of abduction is presented. Abduction is applied on the basis of the examples provided initially and of the hypothesis space to generate queries for missing positive and negative examples. The added examples are treated in the same way again and again until no more examples can be added. The process can be seen as a way of exploring the hypothesis space in order to build backward all the existing proof trees for the initial examples. The abductive completion procedure is used to solve the problems of extensional top-down learning methods. By means of abduction, a solution consistent with respect to the positive and negative examples given initially can always be found, if it exists in the hypothesis space.

Keywords

Logic Program Target Concept Inductive Logic Programming Horn Clause Derivation Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Wien 1995

Authors and Affiliations

  • D. Gunetti
    • 1
  1. 1.University of TurinTurinItaly

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