Learning Domain Theories Using Abstract Beckground Knowledge
Abstract
Substantial machine learning research has addressed the task of learning new knowledge given a (possibly incomplete or incorrect) domain theory, but leaves open the question of where such domain theories originate. In this paper we address the problem of constructing a domain theory from more general, abstract knowledge which may be available. The basis of our method is to first assume a structure for the target domain theory, and second to view background knowledge as constraints on components of that structure. This enables a focusing of search during learning, and also produces a domain theory which is explainable with respect to the background knowledge. We evaluate an instance of this methodology applied to the domain of economics, where background knowledge is represented as a qualitative model.
Cite
Text
Clark and Matwin. "Learning Domain Theories Using Abstract Beckground Knowledge." European Conference on Machine Learning, 1993. doi:10.1007/3-540-56602-3_151Markdown
[Clark and Matwin. "Learning Domain Theories Using Abstract Beckground Knowledge." European Conference on Machine Learning, 1993.](https://mlanthology.org/ecmlpkdd/1993/clark1993ecml-learning/) doi:10.1007/3-540-56602-3_151BibTeX
@inproceedings{clark1993ecml-learning,
title = {{Learning Domain Theories Using Abstract Beckground Knowledge}},
author = {Clark, Peter and Matwin, Stan},
booktitle = {European Conference on Machine Learning},
year = {1993},
pages = {360-365},
doi = {10.1007/3-540-56602-3_151},
url = {https://mlanthology.org/ecmlpkdd/1993/clark1993ecml-learning/}
}