This article performs two types of analysis using Dempster-Shafer theory of belief functions for evidential reasoning. The first analysis deals with the impact of the structure of audit evidence on the overall belief at each variable in the network, variables being the account balance to be audited, the related transaction streams, and the associated audit objectives. The second analysis deals ...

This paper extends a conventional discounting scheme commonly used with the Dempster-Shafer evidential reasoning to deal with conflict. The extended discounting scheme is able to augment, discount, and oppose existing evidence structures when discounting factors take values in different ranges. To show its effectiveness, the scheme is employed for detecting multiple sclerosis (MS) lesions based...

Some recently developed expert systems have used the ShaferDempster theory for reasoning from multiple bodies of evidence. Many expert-system applications require belief to be specified over arbitrary ranges of scalar variables, such as time, distance or sensor measurements. The utility of the existing ShaferDempster theory is limited by the lack of an effective approach for dealing with belief...

This paper introduces a mechanism to quantify propagation of trust in an e-commerce environment. Using principles derived from Dempster-Shafer theory of evidence, we propose a method to propagate and quantify trust. More specifically, we answer the question: if A trusts B, and B trusts C, then with how much certainty may A trust C? This reasoning can be extended to an arbitrary length of trust ...

Dempster/Shafer (D/S) theory has been advocated as a way of representing incompleteness of evidence in a system’s knowledge base. Methods now exist for propagating beliefs through chains of inference. This paper discusses how rules with attached beliefs, a common representation for knowledge in automated reasoning systems, can be transformed into the joint belief functions required by propagati...

When prior probabilities are given as data, there is generally little objection to the use of the Bayes formula or Bayesian networks. On the other hand, when prior probabilities are lacking, Bayesians have the tendency to ignore their ignorance and to make the priors up out of thin air. This leads to decisions in situations where gathering more information would be more appropriate. This paper ...

Plenty of various quality data are available to the officer of watch. The data of various qualities comes from different navigational aids. This kind of data creates new challenge regarding information association. The challenge is met by Mathematical Theory of Evidence. The theory delivers methods enabling combination of various sources of data. Results of association have informative context ...

Mapping ontologies with high precision on the Semantic Web is a challenging problem that needs to be addressed in various domains. One of the main problems with any mapping process, which needs to be applied on different domains is that it always has a certain degree of uncertainty associated with it. In this paper we introduce a method based on Dempster-Shafer theory that use uncertain reasoni...

This paper aims at bridging together the fields of Uncertain Reasoning and Knowledge Representation. The bridge we propose consists in the definition of a Hybrid Belief System, a general modular system capable of performing uncertain reasoning on structured knowledge. This system comprises two distinct modules, UR-mod and KR-mod: the UR-mod provides the uncertainty calculus used to represent un...

We are interested in understanding the relationship between Bayesian inference and evidence theory, in particular imprecise and paradoxical reasoning. The concept of a set of probability distributions is central both in robust Bayesian analysis and in some versions of Dempster-Shafer theory. Most of the literature regards these two theories as incomparable. We interpret imprecise probabilities ...