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Case retrieval nets: Basic ideas and extensions

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KI-96: Advances in Artificial Intelligence (KI 1996)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1137))

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Abstract

An efficient retrieval of a relatively small number of relevant cases from a huge case base is a crucial subtask of Case-Based Reasoning. In this article, we present Case Retrieval Nets (CRNs), a memory model that has recently been developed for this task. The main idea is to apply a spreading activation process to a net-like case memory in order to retrieve cases being similar to a posed query case. We summarize the basic ideas of CRNs, suggest some useful extensions, and present some initial experimental results which suggest that CRNs can successfully handle case bases larger than considered usually in the CBR community.

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Authors and Affiliations

  1. Dept. of Computer Science, Humboldt University Berlin, Axel-Springer-Str. 54a, 10117, Berlin, Germany

    Mario Lenz & Hans-Dieter Burkhard

Authors
  1. Mario Lenz
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  2. Hans-Dieter Burkhard
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Günther Görz Steffen Hölldobler

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© 1996 Springer-Verlag Berlin Heidelberg

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Lenz, M., Burkhard, HD. (1996). Case retrieval nets: Basic ideas and extensions. In: Görz, G., Hölldobler, S. (eds) KI-96: Advances in Artificial Intelligence. KI 1996. Lecture Notes in Computer Science, vol 1137. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61708-6_63

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  • DOI: https://doi.org/10.1007/3-540-61708-6_63

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61708-2

  • Online ISBN: 978-3-540-70669-4

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