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Fusing Probabilistic Information on Maximum Entropy

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KI 2003: Advances in Artificial Intelligence (KI 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2821))

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Abstract

We present a method to fuse pieces of probabilistic information stemming from different sources which is based on information theoretical optimization techniques. We use the well-known principle of maximum entropy to process information most faithfully, while interactions between the different knowledge bases are precluded. The so-defined fusion operator satisfies basic demands, such as commutativity and the Pareto principle. A detailed analysis shows it to merge the corresponding epistemic states. Furthermore, it induces a numerical fusion operator that computes the information theoretical mean of probabilities.

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Author information

Authors and Affiliations

  1. FernUniversität Hagen, Depart. of Computer Science, 58084, Hagen, Germany

    Gabriele Kern-Isberner

  2. Depart. of Economics, FernUniversität Hagen, 58084, Hagen, Germany

    Wilhelm Rödder

Authors
  1. Gabriele Kern-Isberner
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  2. Wilhelm Rödder
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Editor information

Editors and Affiliations

  1. HITeC e. V. Universität Hamburg Fachbereich Informatik, Vogt-Kölln-Str. 30, 22527, Hamburg, Germany

    Andreas Günter

  2. Otto-von-Guericke-University of Magdeburg,  

    Rudolf Kruse

  3. Cognitive Systems Laboratory, Department Informatik, Universität Hamburg, 22527, Hamburg, Germany

    Bernd Neumann

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Kern-Isberner, G., Rödder, W. (2003). Fusing Probabilistic Information on Maximum Entropy. In: Günter, A., Kruse, R., Neumann, B. (eds) KI 2003: Advances in Artificial Intelligence. KI 2003. Lecture Notes in Computer Science(), vol 2821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39451-8_30

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  • DOI: https://doi.org/10.1007/978-3-540-39451-8_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20059-8

  • Online ISBN: 978-3-540-39451-8

  • eBook Packages: Springer Book Archive

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