Integrated Learning with Incorrect and Incomplete Theories
Abstract
This paper discusses incorrect and incomplete theories in integrating empirical and explanation-based learning techniques. The paper focuses on OCCAM, a program which is unique among explanation-based learning systems in that it has the capability to acquire via empirical means the knowledge needed for explanation-based learning. Two major extensions to OCCAM are reported: • The ability to revise a schema formed by explanation-based learning when it becomes apparent that the underlying domain theory was incorrect. • The ability to use empirical learning techniques to acquire a new rule to complete an explanation. These extensions address the issue of utilizing incorrect and incomplete theories in an integrated learning system.
Cite
Text
Pazzani. "Integrated Learning with Incorrect and Incomplete Theories." International Conference on Machine Learning, 1988. doi:10.1016/B978-0-934613-64-4.50035-9Markdown
[Pazzani. "Integrated Learning with Incorrect and Incomplete Theories." International Conference on Machine Learning, 1988.](https://mlanthology.org/icml/1988/pazzani1988icml-integrated/) doi:10.1016/B978-0-934613-64-4.50035-9BibTeX
@inproceedings{pazzani1988icml-integrated,
title = {{Integrated Learning with Incorrect and Incomplete Theories}},
author = {Pazzani, Michael J.},
booktitle = {International Conference on Machine Learning},
year = {1988},
pages = {291-297},
doi = {10.1016/B978-0-934613-64-4.50035-9},
url = {https://mlanthology.org/icml/1988/pazzani1988icml-integrated/}
}