IEICE TRANSACTIONS on Information
Methods for Adapting Case-Bases to Environments
Summary :
In the domains involving environmental changes, some knowledge and heuristics which were useful for solving problems in the previous environment often become unsuitable for problems in the new environment. This paper describes two approaches to solve such problems in the context of case-based reasoning systems. The first one is maintaining descriptions of applicable scopes of cases through generalization and specialization. The generalization is performed to expand problem descriptions, i. e. descriptions of applicable scopes of cases. On the other hand, the specialization is performed to narrow problem descriptions of cases which failed to be applied to given problems with the aim of dealing with environmental changes. The second approach is forgetting, that is deleting obsolete cases from the case-base. However, the domain-dependent knowledge is necessary for testing obsolescence of cases and that causes the problem of knowledge acquisition. We adopt the strategies used by conventional learning systems and extend them using the least domain-dependent knowledge. These two approaches for adapting the case-base to the environment are evaluated through simulations in the domain of electric power systems.
- Publication
- IEICE TRANSACTIONS on Information Vol.E82-D No.10 pp.1393-1400
- Publication Date
- 1999/10/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence and Cognitive Science
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Hiroyoshi WATANABE, Kenzo OKUDA, Katsuhiro YAMAZAKI, "Methods for Adapting Case-Bases to Environments" in IEICE TRANSACTIONS on Information,
vol. E82-D, no. 10, pp. 1393-1400, October 1999, doi: .
Abstract: In the domains involving environmental changes, some knowledge and heuristics which were useful for solving problems in the previous environment often become unsuitable for problems in the new environment. This paper describes two approaches to solve such problems in the context of case-based reasoning systems. The first one is maintaining descriptions of applicable scopes of cases through generalization and specialization. The generalization is performed to expand problem descriptions, i. e. descriptions of applicable scopes of cases. On the other hand, the specialization is performed to narrow problem descriptions of cases which failed to be applied to given problems with the aim of dealing with environmental changes. The second approach is forgetting, that is deleting obsolete cases from the case-base. However, the domain-dependent knowledge is necessary for testing obsolescence of cases and that causes the problem of knowledge acquisition. We adopt the strategies used by conventional learning systems and extend them using the least domain-dependent knowledge. These two approaches for adapting the case-base to the environment are evaluated through simulations in the domain of electric power systems.
URL: https://global.ieice.org/en_transactions/information/10.1587/e82-d_10_1393/_p
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@ARTICLE{e82-d_10_1393,
author={Hiroyoshi WATANABE, Kenzo OKUDA, Katsuhiro YAMAZAKI, },
journal={IEICE TRANSACTIONS on Information},
title={Methods for Adapting Case-Bases to Environments},
year={1999},
volume={E82-D},
number={10},
pages={1393-1400},
abstract={In the domains involving environmental changes, some knowledge and heuristics which were useful for solving problems in the previous environment often become unsuitable for problems in the new environment. This paper describes two approaches to solve such problems in the context of case-based reasoning systems. The first one is maintaining descriptions of applicable scopes of cases through generalization and specialization. The generalization is performed to expand problem descriptions, i. e. descriptions of applicable scopes of cases. On the other hand, the specialization is performed to narrow problem descriptions of cases which failed to be applied to given problems with the aim of dealing with environmental changes. The second approach is forgetting, that is deleting obsolete cases from the case-base. However, the domain-dependent knowledge is necessary for testing obsolescence of cases and that causes the problem of knowledge acquisition. We adopt the strategies used by conventional learning systems and extend them using the least domain-dependent knowledge. These two approaches for adapting the case-base to the environment are evaluated through simulations in the domain of electric power systems.},
keywords={},
doi={},
ISSN={},
month={October},}
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TY - JOUR
TI - Methods for Adapting Case-Bases to Environments
T2 - IEICE TRANSACTIONS on Information
SP - 1393
EP - 1400
AU - Hiroyoshi WATANABE
AU - Kenzo OKUDA
AU - Katsuhiro YAMAZAKI
PY - 1999
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E82-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 1999
AB - In the domains involving environmental changes, some knowledge and heuristics which were useful for solving problems in the previous environment often become unsuitable for problems in the new environment. This paper describes two approaches to solve such problems in the context of case-based reasoning systems. The first one is maintaining descriptions of applicable scopes of cases through generalization and specialization. The generalization is performed to expand problem descriptions, i. e. descriptions of applicable scopes of cases. On the other hand, the specialization is performed to narrow problem descriptions of cases which failed to be applied to given problems with the aim of dealing with environmental changes. The second approach is forgetting, that is deleting obsolete cases from the case-base. However, the domain-dependent knowledge is necessary for testing obsolescence of cases and that causes the problem of knowledge acquisition. We adopt the strategies used by conventional learning systems and extend them using the least domain-dependent knowledge. These two approaches for adapting the case-base to the environment are evaluated through simulations in the domain of electric power systems.
ER -
English
