Explanation-Based Learning: An Alternative View

Gerald Dejong, Raymond Mooney

Research output: Contribution to journalArticle

Abstract

In the last issue of this journal Mitchell, Keller, and Kedar-Cabelli presented a unifying framework for the explanation-based approach to machine learning. While it works well for a number of systems, the framework does not adequately capture certain aspects of the systems under development by the explanation-based learning group at Illinois. The primary inadequacies arise in the treatment of concept operationality, organization of knowledge into schemata, and learning from observation. This paper outlines six specific problems with the previously proposed framework and presents an alternative generalization method to perform explanation-based learning of new concepts.

Original languageEnglish (US)
Pages (from-to)145-176
Number of pages32
JournalMachine Learning
Volume1
Issue number2
DOIs
StatePublished - Jun 1986

Keywords

  • concept acquisition
  • explanation-based learning
  • machine learning

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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