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Knowledge-Intensive Case-Based Reasoning in CREEK

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Advances in Case-Based Reasoning (ECCBR 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3155))

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Abstract

Knowledge-intensive CBR assumes that cases are enriched with general domain knowledge. In CREEK, there is a very strong coupling between cases and general domain knowledge, in that cases are embedded within a general domain model. This increases the knowledge-intensiveness of the cases themselves. A knowledge-intensive CBR method calls for powerful knowledge acquisition and modeling techniques, as well as machine learning methods that take advantage of the general knowledge represented in the system. The focusing theme of the paper is on cases as knowledge within a knowledge-intensive CBR method. This is made concrete by relating it to the CREEK architecture and system, both in general terms, and through a set of example projects where various aspects of this theme have been studied.

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Author information

Authors and Affiliations

  1. Department of Computer and Information Science, Norwegian University of Science and Technology (NTNU), NO-7491, Trondheim, Norway

    Agnar Aamodt

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  1. Agnar Aamodt
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Electronics, Mälardalen University, SE-72123, Västerås, Sweden

    Peter Funk

  2. Facultad de Informática, Universidad Complutense de Madrid, 28040, Madrid, Spain

    Pedro A. González Calero

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Aamodt, A. (2004). Knowledge-Intensive Case-Based Reasoning in CREEK. In: Funk, P., González Calero, P.A. (eds) Advances in Case-Based Reasoning. ECCBR 2004. Lecture Notes in Computer Science(), vol 3155. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28631-8_1

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  • DOI: https://doi.org/10.1007/978-3-540-28631-8_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22882-0

  • Online ISBN: 978-3-540-28631-8

  • eBook Packages: Springer Book Archive

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