Belief revision and information fusion on optimum entropy

Authors

  • Gabriele Kern-Isberner,

    Corresponding author
    1. Department of Computer Science, FernUniversität Hagen, 58084 Hagen, Germany
    • Author to whom all correspondence should be addressed: e-mail: gabriele.kern-isberner@fernuni-hagen.de
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  • Wilhelm Rödder

    Corresponding author
    1. Department of Economics, FernUniversität Hagen, Germany
    • email: wilhelm.roedder@fernuni-hagen.de.
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Abstract

This article presents new methods for probabilistic belief revision and information fusion. By making use of the information theoretical principles of optimum entropy (ME principles), we define a generalized revision operator that aims at simulating the human learning of lessons, and we introduce a fusion operator that handles probabilistic information faithfully. This ME-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. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 837–857, 2004.

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