Improving Learning Using Causality and Abduction
Abstract
This paper presents a system which learns and maintains a diagnostic knowledge base using a causal model of the domain, a body of phenomenological theory and a set of examples. The phenomenological knowledge is used deductively, the causal model is used abductively and the examples are used inductively. The problems of imperfection and intractability of the theory are handled by allowing the system to make assumptions during its reasoning. In this way, robust knowledge can be learned with limited complexity and limited number of examples. An example in the domain of heat transfer is presented.
Cite
Text
Botta et al. "Improving Learning Using Causality and Abduction." International Conference on Machine Learning, 1991. doi:10.1016/B978-1-55860-200-7.50098-2Markdown
[Botta et al. "Improving Learning Using Causality and Abduction." International Conference on Machine Learning, 1991.](https://mlanthology.org/icml/1991/botta1991icml-improving/) doi:10.1016/B978-1-55860-200-7.50098-2BibTeX
@inproceedings{botta1991icml-improving,
title = {{Improving Learning Using Causality and Abduction}},
author = {Botta, Marco and Ravotto, S. and Saitta, Lorenza and Sperotto, S. B.},
booktitle = {International Conference on Machine Learning},
year = {1991},
pages = {480-484},
doi = {10.1016/B978-1-55860-200-7.50098-2},
url = {https://mlanthology.org/icml/1991/botta1991icml-improving/}
}