Default reasoning can provide a means of deriving plausible semantic conclusion under imprecise and contradictory information in forensic visual surveillance. In such reasoning under uncertainty, proper uncertainty handling formalism is required. A discrete species of Bilattice for multivalued default logic demonstrated default reasoning in visual surveillance. In this article, the authors pres...

Combining testimonial reports from independent and partially reliable information sources is an important epistemological problem of uncertain reasoning. Within the framework of Dempster–Shafer theory, we propose a general model of partially reliable sources, which includes several previously known results as special cases. The paper reproduces these results on the basis of a comprehensive mode...

The Dempster-Shafer “theory of evidence” encompasses and extends the Bayes Theorem-based decision making machinery. Dempster-Shafer’s innovation is the introduction of lower and upper bounds, designated “belief” and “plausibility”, that are attached to probability estimates. The Dempster-Shafer algebra provides for propagation of and reasoning about these quantities according to an algebra whos...

Linear constraints occur naturally in many reasoning problems and the information that they represent is often uncertain. There is a difficulty in applying many AI uncertainty formalisms to this situation, as their representation of the underlying logic, either as a mutually exclusive and exhaustive set of possibilities, or with a propositional or a predicate logic, is inappropriate (or at leas...

Abstract In Evidence theory, several conditioning rules for updating belief have been proposed, including Dempster’s rule of conditioning. The paper views the conditioning rules proposed so far and proposes a new rule of conditioning based on three requirements. Then, it generalizes the rule to be applied to the case where condition is given by an uncertain belief. The paper also discusses a fe...

[1] advocate the use of Dempster-Shafer (0-5) theory in evidence-gathering process. It is stated that they are unaware of any formal model which could allow inexact reasoning at whatever level of abstraction. [2] later shows how evidential reasoning can be conducted in the same hypolhesis space using a Bayesian model. The purpose of this note is to examine the difference of theses two schemes, ...

For medical diagnosis, fuzzy Dempster-Shafer theory is extended to model domain knowledge under probabilistic and fuzzy uncertainty. However, there are some information loss using discrete fuzzy sets and traditional matching degree method. This study aims to provide a new evidential structure to reduce information loss. This paper proposes a new intuitionistic fuzzy evidential reasoning (IFER) ...

In this paper some initial work towards a new approach to qualitative reasoning under un­ certainty is presented. This method is not only applicable to qualitative probabilistic reasoning, as is the case with other methods, but also allows the qualitative propagation within networks of values based upon possi­ bility theory and Dempster-Shafer evidence theory. The method is applied to two simpl...

In Multi-Agent System (MAS) competitive environment, agents’ reasoning process is more complex due to the fact that uncertainty factor is involved. Decision making models and mechanisms for MAS are studied in this paper from two viewpoints: S-R-K human information processing model viewpoint, and game theory and decision theory viewpoint. Five decision making modules that are rule-based module, ...

This paper describes a theory of reasoning about uncertainly, based on a representation of state'i of certainly called endorsements (see Cohen and Grinberg, 1983, for a more detailed discussion of the theory.) The theory of endorsements is an alternative to numerical methods for reasoning about uncertainty, such as subjective Bayesian methods (Shortliffe and Buchanan, 1975; Duda, Hart, and Nils...