Belief Revision and Information Fusion in a Probabilistic Environment

Gabriele Kern-Isberner and Wilhelm Rödder

This paper presents new methods for probabilistic belief revision and information fusion. By making use of the principles of optimum entropy (ME-principles), we define a generalized revision operator which aims at simulating the human learning of lessons, and we introduce a fusion operator which handles probabilistic information faithfully. In general, this fusion operator computes kind of mean probabilistic values from pieces of information provided by different sources.


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