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Proceedings Paper

Efficiently applying uncertain implication rules to the transferable belief model
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Paper Abstract

This paper addresses the use of implication rules (with uncertainty) within the Transferable Belief Model (TBM) where the rules convey knowledge about relationships between two frames of discernment. Technical challenges include: a) computational scalability of belief propagation, b) logical consistency of the rules, and c) uncertainty of the rules. This paper presents a simplification of the formalism developed by Ristic and Smets for incorporating uncertain implication rules into the TBM. By imposing two constraints on the form of implication rules, and restricting results to singletons of the frame of discernment, we derive a belief function that can be evaluated in polynomial time.

Paper Details

Date Published: 29 May 2013
PDF: 8 pages
Proc. SPIE 8756, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2013, 875606 (29 May 2013); doi: 10.1117/12.2014782
Show Author Affiliations
William J. Farrell, Lakota Technical Solutions, Inc. (United States)
Andrew M. Knapp, Lakota Technical Solutions, Inc. (United States)


Published in SPIE Proceedings Vol. 8756:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2013
Jerome J. Braun, Editor(s)

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