Belief revision and information fusion on optimum entropy | Zendy

KernIsberner Gabriele | Zendy; Rödder Wilhelm | Zendy

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Belief revision and information fusion on optimum entropy

Author(s) -

KernIsberner Gabriele,

Rödder Wilhelm

Publication year - 2004

Publication title -

international journal of intelligent systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.291

H-Index - 87

eISSN - 1098-111X

pISSN - 0884-8173

DOI - 10.1002/int.20027

Subject(s) - merge (version control) , probabilistic logic , information fusion , computer science , belief revision , entropy (arrow of time) , fusion , operator (biology) , mutual information , cross entropy , commutative property , pareto principle , theoretical computer science , artificial intelligence , algorithm , mathematics , principle of maximum entropy , information retrieval , mathematical optimization , discrete mathematics , linguistics , philosophy , physics , biochemistry , chemistry , repressor , quantum mechanics , transcription factor , gene

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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