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

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Part of the Lecture Notes in Computer Science book series (LNAI,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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Walker, T. et al. (2011). Automating the ILP Setup Task: Converting User Advice about Specific Examples into General Background Knowledge. In: Frasconi, P., Lisi, F.A. (eds) Inductive Logic Programming. ILP 2010. Lecture Notes in Computer Science(), vol 6489. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21295-6_28

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  • DOI: https://doi.org/10.1007/978-3-642-21295-6_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21294-9

  • Online ISBN: 978-3-642-21295-6

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