Adaptation Using Iterated Estimations

摘要

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.