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Dempster-Shafer Theory

1984
This is a theory of evidence potentially suitable for knowledge-based systems. The system is based on “basic probabilities” which can be visualized as probability masses that are constrained to stay within the subset with which they are associated, but are free to move over every point in the subset.
Alan Bundy, Lincoln Wallen
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Fast algorithms for Dempster-Shafer theory

2005
The runtime of the usual algorithms computing the transformation of a basic belief assignment into its associated belief function and conversely is an exponential function of the cardinality of (the domain of) the basic belief assignment. In this paper, new algorithms with a polynomial runtime are presented. These algorithms appear to be optimal in the
Kennes, Robert, Smets, Philippe
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Nonstandard analysis and Dempster-Shafer theory

International Journal of Intelligent Systems, 2000
Summary: The theorems, propositions, and examples were concerned about the Dempster-Shafer random variable. Future areas of investigation can include determining theorems analogous to the classical theorems of probability. Also, as more theory is developed, applications of the Dempster-Shafer random variable can be investigated.
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Analyzing a Paradox in Dempster-Shafer Theory

2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery, 2008
Zadeh has criticized Dempster-Shafer (D-S) theory with a famous counter-example, where an almost impossible case based on two bodies of evidence produces a certain outcome by Dempster's rule of combination. This paper investigates this intuitive paradox by justifying Dempster's rule of combination under Shafer's interpretation for D-S theory.
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Evidential logic and Dempster-Shafer theory

Proceedings of the ACM SIGART international symposium on Methodologies for intelligent systems, 1986
Most artificial intelligence applications require reasoning with uncertainty and incompleteness. Such information is not captured in terms of simple true and false values nor in terms of probabilistic estimates when relevant statistical data are lacking.
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The Dempster-Shafer Theory

2009
The initial work introducing Dempster-Shafer (D-S) theory is found in Dempster (1967) and Shafer (1976). Since its introduction the very name causes confusion, a more general term often used is belief functions (both used intermittently here). Nguyen (1978) points out, soon after its introduction, that the rudiments of D-S theory can be considered ...
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On a Special Class of Dempster-Shafer Theories

2004
In this paper we want to draw Reader’s attention to the issue of impact of separate measurement of features (attributes) from which we want to make inferences. It turns out, that the fact of separate measurements implies algorithmic simplifications for many forms of reasoning in DST. Basic theorems and algorithms exploiting this are given.
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AN EXERCISE IN DEMPSTER-SHAFER THEORY

International Journal of General Systems, 1992
We discuss a concrete example of the use of the Dempster rule presented by Weichselberger and Pohlmann15 and show how their approach has to be modified to yield an intuitively adequate result. At the end we mention a possibility of a similar modification of their approach to MYCIN-like systems.
P. HAJEK, D. HARMANEC
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Fundamentals of the Dempster-Shafer theory

SPIE Proceedings, 2012
In this paper, I discuss the basic notions of the Dempster Shafer theory. Using a simple engineering example, I highlight sources of confusion in the Dempster Shafer literature, and some questions that arise in the course of applying the Dempster Shafer algorithm. Finally, I discuss the measure theoretic foundation that reveals the intimate connections
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Default logic and Dempster-Shafer theory

2005
A new version of Reiter's Default Logic is developed which has a number of advantages: it is computationally much simpler and has some more intuitive properties, such as cumulativity. Furthermore, it is shown that this Default Logic is a limiting case of a Dempster-Shafer framework, thereby demonstrating a strong connection between two apparently very ...
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