Explanation-Based Learning with Week Domain Theories
Abstract
This chapter reviews explanation-based learning with weak domain theories. One facet of a weak domain theory is that the influence of a number of factors is known. A weak theory does not provide any means of combining these influences. The theory constrains features that play a part in predictive relationships. Only when an accurate predictive relationship cannot be made by considering combinations of known influences are other factors considered. A learning system called PostHoc has been developed that uses this sort of background knowledge to propose hypotheses that are then tested against further data. PostHoc utilizes a weak domain theory to generate plausible explanations for a state change after it has occurred. This background knowledge is also used to revise hypotheses that fail to make accurate predictions.
Cite
Text
Pazzani. "Explanation-Based Learning with Week Domain Theories." International Conference on Machine Learning, 1989. doi:10.1016/B978-1-55860-036-2.50026-6Markdown
[Pazzani. "Explanation-Based Learning with Week Domain Theories." International Conference on Machine Learning, 1989.](https://mlanthology.org/icml/1989/pazzani1989icml-explanation/) doi:10.1016/B978-1-55860-036-2.50026-6BibTeX
@inproceedings{pazzani1989icml-explanation,
title = {{Explanation-Based Learning with Week Domain Theories}},
author = {Pazzani, Michael J.},
booktitle = {International Conference on Machine Learning},
year = {1989},
pages = {72-74},
doi = {10.1016/B978-1-55860-036-2.50026-6},
url = {https://mlanthology.org/icml/1989/pazzani1989icml-explanation/}
}