CABOT: An Adaptive Approach to Case-Based Search
Proceedings of the Twelfth International Joint Conference on Artificial Intelligence. Learning Be Knowledge Acquisition. Pages 803-808.
Abstract
This paper describes CABOT, a case-based system that is able to adjust its retrieval and adaptation metrics, in addition to storing cases. It has been applied to the game of OTHELLO . Experiments show that CABOT saves about half as many cases as similar systems that do not adjust their retrieval and adaptation mechanisms. It also consistently beats these systems. These results suggest that existing case-based systems could save fewer cases without reducing their current levels of performance. They also demonstrate that it is beneficial to distinguish failures due to missing information, faulty retrieval, and faulty adaptation.
@inproceedings{ijcai1991p118,
title = {CABOT: An Adaptive Approach to Case-Based Search},
author = {Callan, James P. and Fawcett, Tom and Rissland, Edwina L.},
booktitle = {Proceedings of the Twelfth International Joint Conference on
Artificial Intelligence, {IJCAI-91}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {John Mylopoulos and Ray Reiter},
pages = {803--808},
year = {1991},
month = {8},
note = {Learning Be Knowledge Acquisition},
url = {https://ijcai.org/proceedings/1991/118},
}
TY - CPAPER TI - CABOT: An Adaptive Approach to Case-Based Search AU - Callan, James P. AU - Fawcett, Tom AU - Rissland, Edwina L. T2 - Proceedings of the Twelfth International Joint Conference on Artificial Intelligence, IJCAI-91 PB - International Joint Conferences on Artificial Intelligence PY - 1991 SP - 803 EP - 808 UR - https://ijcai.org/proceedings/1991/118 ER -Copied
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