Adaptation rule learning for case-based reasoning

  • Authors:

  • Huan Li;Xin Li;Dawei Hu;Tianyong Hao;Liu Wenyin;Xiaoping Chen

  • Affiliations:

  • Dept. of Comp. Sci. and Technol., Univ. of Science & Technology of China, Hefei and Joint Res. Lab of Excellence, CityU-USTC Advan. Res. Inst., Suzhou and Dept. of Comp. Sci. City Univ. of Hong Ko ...;Dept. of Comp. Sci. and Technol., Univ. of Science & Technology of China, Hefei and Joint Res. Lab of Excellence, CityU-USTC Advan. Res. Inst., Suzhou and Dept. of Comp. Sci. City Univ. of Hong Ko ...;Dept. of Comp. Sci. and Technol., Univ. of Science & Technology of China, Hefei and Joint Res. Lab of Excellence, CityU-USTC Advan. Res. Inst., Suzhou and Dept. of Comp. Sci. City Univ. of Hong Ko ...;Department of Computer Science, City University of Hong Kong, Hong Kong, China;Joint Research Lab of Excellence, CityU-USTC Advanced Research Institute, Suzhou, China and Department of Computer Science, City University of Hong Kong, Hong Kong, China;Department of Computer Science and Technology, University of Science & Technology of China, Hefei, China and Joint Research Lab of Excellence, CityU-USTC Advanced Research Institute, Suzhou, China

  • Venue:
  • Concurrency and Computation: Practice & Experience - Web 2.0, Semantics, Knowledge and Grid
  • Year:
  • 2009

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Abstract

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.