Abstract
In this paper, we present a framework for learning adaptation
knowledge which we call knowledge light approaches for
case-based reasoning (CBR) systems. Knowledge light means that
these approaches use already acquired knowledge inside the CBR
system. Therefore, 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.
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