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Engineering and Learning of Adaptation Knowledge in Case-Based Reasoning

  • Amélie Cordier
  • Béatrice Fuchs
  • Alain Mille
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4248)

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.

Keywords

Problem Descriptor Source Case Target Case Knowledge Unit Retrieval Step 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Amélie Cordier
    • 1
  • Béatrice Fuchs
    • 1
  • Alain Mille
    • 1
  1. 1.LIRIS UMR 5205CNRS/INSA de Lyon/Université Claude Bernard Lyon 1/, Université Lumière Lyon 2/Ecole Centrale de LyonVILLEURBANNE

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