A Framework for Learning Adaptation Knowledge Based on Knowledge Light Approaches

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 different knowledge containers. Next we present our framework in terms of these knowledge containers. Further, we apply our framework to two very different 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 for further research. Finally we close with a short discussion.