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European Workshop on Case-Based Reasoning

EWCBR 1993: Topics in Case-Based Reasoning pp 144–154Cite as

Massively parallel case-based reasoning with probabilistic similarity metrics

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 837))

Abstract

We propose a probabilistic case-space metric for the case matching and case adaptation tasks. Central to our approach is a probability propagation algorithm adopted from Bayesian reasoning systems, which allows our case-based reasoning system to perform theoretically sound probabilistic reasoning. The same probability propagation mechanism actually offers a uniform solution to both the case matching and case adaptation problems. We also show how the algorithm can be implemented as a connectionist network, where efficient massively parallel case retrieval is an inherent property of the system. We argue that using this kind of an approach, the difficult problem of case indexing can be completely avoided.

This research was supported by Technology Development Center (TEKES) and Honkanen Foundation.

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Authors and Affiliations

  1. Department of Computer Science, University of Helsinki, P.O.Box 26, FIN-00014, Finland

    Petri Myllymäki & Henry Tirri

Authors
  1. Petri Myllymäki
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  2. Henry Tirri
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Stefan Wess Klaus-Dieter Althoff Michael M. Richter

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© 1994 Springer-Verlag Berlin Heidelberg

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Myllymäki, P., Tirri, H. (1994). Massively parallel case-based reasoning with probabilistic similarity metrics. In: Wess, S., Althoff, KD., Richter, M.M. (eds) Topics in Case-Based Reasoning. EWCBR 1993. Lecture Notes in Computer Science, vol 837. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58330-0_83

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  • DOI: https://doi.org/10.1007/3-540-58330-0_83

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-58330-1

  • Online ISBN: 978-3-540-48655-8

  • eBook Packages: Springer Book Archive

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