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Adaptation rule learning for case‐based reasoning
Author(s) -
Li Huan,
Li Xin,
Hu Dawei,
Hao Tianyong,
Wenyin Liu,
Chen Xiaoping
Publication year - 2009
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.1368
Subject(s) - case based reasoning , adaptation (eye) , computer science , process (computing) , similarity (geometry) , set (abstract data type) , artificial intelligence , resource (disambiguation) , knowledge base , domain (mathematical analysis) , reuse , machine learning , space (punctuation) , rule based system , data mining , mathematics , engineering , computer network , mathematical analysis , physics , waste management , optics , image (mathematics) , programming language , operating system
A method of learning adaptation rules for case‐based reasoning (CBR) is proposed in this paper. The resource space model and the semantic link network are applied in case‐base construction for efficient resource management and reuse. Adaptation rules are generated from the case‐base with the guidance of domain knowledge, which is also extracted from the case‐base. The adaptation rules are refined before they are applied in the revision process. General domain knowledge is brought in to help accurate similarity computing. After solving each new problem, the adaptation rule set is updated by an evolution module in the retention process. The results of our experiment show that the obtained adaptation rules can improve the performance of the CBR system compared with a retrieval‐only CBR system. The average solution difference error is decreased by 46.56%. Copyright © 2008 John Wiley & Sons, Ltd.