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Proceedings Paper

Exponential growth rate of Dempster-Shafer belief functions
Author(s): Shijie Wang; Marco Valtorta
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Paper Abstract

In this paper we discuss some topics of knowledge representation for reasoning under uncertainty in belief network-based systems. In particular, we will examine the seemly obvious but unduly overlooked phenomenon that belief functions can grow at an exponential rate when using Dempster's rule of combination, which often results in belief computations being done in nearly worst cases. This problem has severe practical consequences for the development of belief network-based systems in applications domains where the knowledge structure determines a dense belief network with high degrees of node linkage and especially with large clusterings of belief functions. Empirical evidence suggests that belief networks for some types of problem domain like classification tend to be very dense. Despite the development of efficient local computation schemes for belief propagation in general Dempster-Shafer belief networks, it can be concluded that the actual applicability of belief networks is limited subject to the knowledge structure of the problem domain under consideration.

Paper Details

Date Published: 1 March 1992
PDF: 10 pages
Proc. SPIE 1707, Applications of Artificial Intelligence X: Knowledge-Based Systems, (1 March 1992); doi: 10.1117/12.56868
Show Author Affiliations
Shijie Wang, Univ. of South Carolina (United States)
Marco Valtorta, Univ. of South Carolina (United States)

Published in SPIE Proceedings Vol. 1707:
Applications of Artificial Intelligence X: Knowledge-Based Systems
Gautam Biswas, Editor(s)

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