An Integrated Knowledge Adaption Framework for Case-based Reasoning Systems

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Conference Proceeding
Knowledge-Based and Intelligent Information and Engineering Systems: Lecture Notes in Artificial Intelligence Vol 5712, 2009, pp. 372 - 379
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The development of effective knowledge adaption techniques is one of the promising solutions to improve the performance of case-based reasoning (CBR) systems. Case-base maintenance becomes a powerful method to refine knowledge in CBR systems. This paper proposes an integrated knowledge adaption framework for CBR systems which contains a meta database component and a maintenance strategies component. The meta database component can help track changes of interested concepts and therefore enable a CBR system to signal a need for maintenance or to invoke adaption on its own. The maintenance strategies component can perform cross-container maintenance operations in a CBR system. This paper also illustrates how the proposed integrated knowledge adaption framework assists decision makers to build dynamic prediction and decision capabilities.
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