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Automating the ILP Setup Task: Converting User Advice about Specific Examples into General Background Knowledge

  • Trevor Walker
  • Ciaran O’Reilly
  • Gautam Kunapuli
  • Sriraam Natarajan
  • Richard Maclin
  • David Page
  • Jude Shavlik
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6489)

Abstract

Inductive Logic Programming (ILP) provides an effective method of learning logical theories given a set of positive examples, a set of negative examples, a corpus of background knowledge, and specification of a search space (e.g., via mode definitions) from which to compose the theories. While specifying positive and negative examples is relatively straightforward, composing effective background knowledge and search-space definition requires detailed understanding of many aspects of the ILP process and limits the usability of ILP. We introduce two techniques to automate the use of ILP for a non-ILP expert. These techniques include automatic generation of background knowledge from user-supplied information in the form of a simple relevance language, used to describe important aspects of specific training examples, and an iterative-deepening-style search process.

Keywords

Advice Taking Human Teaching of Machines 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Trevor Walker
    • 1
  • Ciaran O’Reilly
    • 2
  • Gautam Kunapuli
    • 1
  • Sriraam Natarajan
    • 1
  • Richard Maclin
    • 3
  • David Page
    • 1
  • Jude Shavlik
    • 1
  1. 1.University of WisconsinMadisonUSA
  2. 2.SRI InternationalUSA
  3. 3.University of MinnesotaDuluthUSA

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