The objective of this paper is to describe the potential o€ered by the Dempster±Shafer theory (DST) of evidence as a promising improvement on ``traditional'' approaches to decision analysis. Dempster±Shafer techniques originated in the work of Dempster on the use of probabilities with upper and lower bounds. They have subsequently been popularised in the literature on Arti®cial Intelligence (AI...

The Dempster-Shafter Theory is well-known for its usefulness to express uncertain judgments of experts. This contribution shows how to apply the calculus to safety and reliability modelling, especially to expert judgement; Failure Modes, Effects, and Criticality Analysis; Event Tree Analysis, Fault Tree Analysis, and Reliability Centred Maintenance. Including a tutorial introduction to the Demp...

The spatial prediction of land cover at the field scale in winter appears useful for the issue of bare soils reduction in agricultural intensive regions. High variability of the factors that motivate the land cover changes between each winter involves integration of uncertainty in the modelling process. Fusion process with Dempster-Shafer Theory (DST) presents some limits in generating errors i...

The importance of the Dempster-Shafer theory (DST) for modeling and propagating uncertainty has grown in the recent past. An obstacle for wider application of this theory in industrial practice is the lack of software support for analysts. The few tools available depend on floating point arithmetic and do not consider the inherently interval-based nature of the DST to the full extent. Therefore...

The origins of Dempster-Shafer theory (DST) go back to the work by Dempster (1967) who developed a system of upper and lower probabilities. Following this, his student Shafer (1976), in his book “A Mathematical Theory of Evidence” added to Dempster’s work, including a more thorough explanation of belief functions. In summary, it is a methodology for evidential reasoning, manipulating uncertaint...

In this paper, target differentiation based on pattern of data which are obtained by a set of two ultrasonic sensors is considered. A neural network based target classifier is applied to these data to categorize the data of each sensor. Then the results are fused together by Dempster–Shafer theory (DST) and Dezert–Smarandache theory (DSmT) to make final decision. The Generalized Aggregated Unce...

Multimodal biometric technology relatively is a technology developed to overcome those limitations imposed by unimodal biometric systems. The paradigm consolidates evidence from multiple biometric sources offering considerable improvements in reliability with reasonably overall performance in many applications. Meanwhile, the issue of efficient and effective information fusion of these evidence...

In this paper, we review recent applications of Dempster-Shafer theory (DST) of belief functions to auditing and business decision-making. We show how DST can better map uncertainties in the application domains than Bayesian theory of probabilities. We review the applications in auditing around three practical problems that challenge the effective application of DST, namely, hierarchical eviden...

In this paper, five conditions that have been proposed by Cobb and Shenoy are studied for nine different mappings from the Dempster-Shafer theory to the probability theory. After comparing these mappings, one of the considerable results indicates that none of the mappings satisfies the condition of invariance with respect to the marginalization process. In more details, the main reason for this...