Abstract
Case-based reasoning (CBR) uses various knowledge containers for problem solving: cases, domain, similarity, and adaptation knowledge. These various knowledge containers are characterised from the engineering and learning points of view. We focus on adaptation and similarity knowledge containers that are of first importance, difficult to acquire and to model at the design stage. These difficulties motivate the use of a learning process for refining these knowledge containers. We argue that in an adaptation guided retrieval approach, similarity and adaptation knowledge containers must be mixed. We rely on a formalisation of adaptation for highlighting several knowledge units to be learnt, i.e. dependencies and influences between problem and solution descriptors. Finally, we propose a learning scenario called “active approach” where the user plays a central role for achieving the learning steps.
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Cordier, A., Fuchs, B., Mille, A. (2006). Engineering and Learning of Adaptation Knowledge in Case-Based Reasoning. In: Staab, S., Svátek, V. (eds) Managing Knowledge in a World of Networks. EKAW 2006. Lecture Notes in Computer Science(), vol 4248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11891451_27
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DOI: https://doi.org/10.1007/11891451_27
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