An Automatic Learning of Adaptation Knowledge for Case-Based Reasoning 


Vol. 6,  No. 1, pp. 96-106, Jan.  1999
10.3745/KIPSTE.1999.6.1.96


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  Abstract

Case-Base Reasoning(CBR) solves the new problems by reusing the solutions to previously solved problems. But, there are differences between perviously known case and a new problem. To solve this problem, Case-Based System have to adapt the solution of the case to suit a new situation. In current CBR systems, case adaptation is usually performed by rule-based method that use rules hand-coded by the system developer. So, CBR system designer faces knowledge acquisition bottleneck akin to those found in traditional expert system design. To solve this problem, in this thesis, we present an automatic learning method of case adaptation knowledge using case base. We use a method of comparing cases in the case base to learn adaptation knowledge. The system is tested in the domain for the decision of travel-price. The result show accuracy improvement in comparison with case retrieval-only system.

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[IEEE Style]

L. J. Pil, C. K. Dal, K. K. Tae, "An Automatic Learning of Adaptation Knowledge for Case-Based Reasoning," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 6, no. 1, pp. 96-106, 1999. DOI: 10.3745/KIPSTE.1999.6.1.96.

[ACM Style]

Lee Jae Pil, Cho Kyoung Dal, and Kim Ki Tae. 1999. An Automatic Learning of Adaptation Knowledge for Case-Based Reasoning. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 6, 1, (1999), 96-106. DOI: 10.3745/KIPSTE.1999.6.1.96.

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