Abstract
A major challenge for case-based reasoning (CBR) is to overcome the knowledge-engineering problems incurred by developing adaptation knowledge. This paper describes an approach to automating the acquisition of adaptation knowledge overcoming many of the associated knowledge-engineering costs. This approach makes use of inductive techniques, which learn adaptation knowledge from case comparison. We also show how this adaptation knowledge can be usefully applied. The method has been tested in a property-evaluation CBR system and the technique is illustrated by examples taken from this domain. In addition, we examine how any available domain knowledge might be exploited in such an adaptation-rule learning-system.
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© 1996 Springer-Verlag Berlin Heidelberg
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Hanney, K., Keane, M.T. (1996). Learning adaptation rules from a case-base. In: Smith, I., Faltings, B. (eds) Advances in Case-Based Reasoning. EWCBR 1996. Lecture Notes in Computer Science, vol 1168. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020610
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DOI: https://doi.org/10.1007/BFb0020610
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-61955-0
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