Adaptation Rule Learning for Case-Based Reasoning

A method of learning adaptation rules for case- based reasoning (CBR) is proposed in this paper. 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. After solving each new problem, the adaptation rule set is updated by an evolution module in the retention process. The results of preliminary experiment show that the adaptation rules obtained could improve the performance of the CBR system compared to a retrieval-only CBR system.

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