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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.