Using Knowledge Containers to Model a Framework for Learning Adaptation Knowledge

In this paper we present a framework for learning adaptation knowledge which knowledge light approaches for case-based reasoning (CBR) systems. Knowledge light means that these approaches use already acquired knowledge inside the CBR system. Therefor we describe the sources of knowledge inside a CBR system along the different knowledge containers. After that we present our framework in terms of these knowledge containers. Further we apply our framework in a case study to one knowledge light approach for learning adaptation knowledge. After that we point on 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 and an outlook to further work