Adaptation Using Iterated Estimations

Gran Falkman

Department of Computer Science, University of Skvde,
P0 Box 408, SE541 28 Skvde, Sweden
goran.falkman@ida.his.se



Abstract. A model for adaptation in case-based reasoning (cBR) is presented. 
Similarity assessment is based on the computation and the iterated 
estimation of structural relationships among representations, and
adaptation is given as a special case of the general process.
Compared to traditional approaches to adaptation within CBR, the presented 
model has the advantage of using a uniform declarative model for
both case representation, similarity assessment and adaptation. As a consequence, 
adaptation knowledge can be made directly available during
similarity assessment and for explanation purposes. The use of a uniform
model also provides the possibility of a CBR approach to adaptation.
The model is compared with other approaches to adaptation within CBR.
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