29 May 2013 Efficiently applying uncertain implication rules to the transferable belief model
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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.
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William J. Farrell, Andrew M. Knapp, "Efficiently applying uncertain implication rules to the transferable belief model", Proc. SPIE 8756, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2013, 875606 (29 May 2013); doi: 10.1117/12.2014782; https://doi.org/10.1117/12.2014782
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