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A Framework for Learning Adaptation KnowledgeBased on Knowledge Light

In this paper, we present a framework for learning adaptation knowledge with knowledge light approaches for case-based reasoning (CBR) systems. \Knowledge light" means that these approaches use knowledge already acquired and represented inside the CBR system. Therefore, we describe the sources of knowledge inside a CBR system along with the diierent knowledge containers. Next we present our framework in terms of these knowledge containers. Further, we apply our framework to two very diierent knowledge light approaches for learning adaptation knowledge. After that we point out some issues which should be addressed during the design or the use of such algorithms for learning adaptation knowledge. From our point of view, many of these issues should be the topic of further research. Finally we close with a short discussion.