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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationInductive Logic Programming - 20th International Conference, ILP 2010, Revised Papers
Pages253-268
Number of pages16
DOIs
StatePublished - Jun 23 2011
Event20th International Conference on Inductive Logic Programming, ILP 2010 - Florence, Italy
Duration: Jun 27 2010Jun 30 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6489 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other20th International Conference on Inductive Logic Programming, ILP 2010
CountryItaly
CityFlorence
Period6/27/106/30/10

Keywords

  • Advice Taking
  • Human Teaching of Machines

Fingerprint Dive into the research topics of 'Automating the ILP setup task: Converting user advice about specific examples into general background knowledge'. Together they form a unique fingerprint.

Cite this