ML Anthology

Multi-Strategy Learning in Nonhomongeneous Domain Theories

Gheorghe Tecuci, Yves Kodratoff
ICML 1989 pp. 14-16
doi:10.1016/B978-1-55860-036-2.50008-4 /icml/1989/tecuci1989icml-multi/

Abstract

This paper presents DISCIPLE, a system illustrating a theory and a methodology for learning expert knowledge in the context of a nonhomogeneous domain theory (Kodratoff & Tecuci, 1987). DISCIPLE integrates a learning system and an empty expert system, both using the same knowledge base. It is initially provided with a nonhomogeneous domain theory and learns problem solving rules from the problem solving steps received from its expert user, during interactive problem solving sessions. In the context of a complete theory about the example, DISCIPLE uses explanation-based learning to improve its performance. In the context of a weak theory about the example, it combines explanation-based learning, learning by analogy, and empirical learning.

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Cite

Text

Tecuci and Kodratoff. "Multi-Strategy Learning in Nonhomongeneous Domain Theories." International Conference on Machine Learning, 1989. doi:10.1016/B978-1-55860-036-2.50008-4

Markdown

[Tecuci and Kodratoff. "Multi-Strategy Learning in Nonhomongeneous Domain Theories." International Conference on Machine Learning, 1989.](https://mlanthology.org/icml/1989/tecuci1989icml-multi/) doi:10.1016/B978-1-55860-036-2.50008-4

BibTeX

@inproceedings{tecuci1989icml-multi,
  title     = {{Multi-Strategy Learning in Nonhomongeneous Domain Theories}},
  author    = {Tecuci, Gheorghe and Kodratoff, Yves},
  booktitle = {International Conference on Machine Learning},
  year      = {1989},
  pages     = {14-16},
  doi       = {10.1016/B978-1-55860-036-2.50008-4},
  url       = {https://mlanthology.org/icml/1989/tecuci1989icml-multi/}
}