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Adaptation Using Iterated Estimations

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Advances in Case-Based Reasoning (ECCBR 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2416))

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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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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Skövde, PO Box 408, SE-541 28, Skövde, Sweden

    Göran Falkman

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  1. Göran Falkman
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Editor information

Editors and Affiliations

  1. School of Computing, The Robert Gordon University, St. Andrew Street, Aberdeen, AB25 1HG, Scotland, UK

    Susan Craw

  2. Computing Science Department, University of Aberdeen, Aberdeen, AB24 3UE, Scotland, UK

    Alun Preece

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© 2002 Springer-Verlag Berlin Heidelberg

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Falkman, G. (2002). Adaptation Using Iterated Estimations. In: Craw, S., Preece, A. (eds) Advances in Case-Based Reasoning. ECCBR 2002. Lecture Notes in Computer Science(), vol 2416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46119-1_8

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  • DOI: https://doi.org/10.1007/3-540-46119-1_8

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44109-0

  • Online ISBN: 978-3-540-46119-7

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