This reply to papers by Pearl and Shafer focuses on two issues underlying the debate on the validity o f using Dempster-Shafer theory, namely the requirement of a process-independent semantics and the a priori need for multiple uncertainty calculi. Pearl shows deficiencies of Dempster-Shafer theory in dealing with several instances o f commonsense reasoning in a process-independent manner. Alth...

We formulate Dempster Shafer Belief functions in terms of Propositional Logic, using the im­ plicit notion of provability underlying Demp­ ster Shafer Theory. The assignment of weights to the propositional literals enables the Belief functions to be explicitly computed using Net­ work Reliability techniques. Also, the updat­ ing of Belief functions using Dempster's Rule of Combination correspon...

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 through distributions of random sets. More furt...

An intrusion detection system (IDS) detects unauthorized manipulations of computer systems. Operation as feature reduction (including feature extraction and feature selection) plays an important role in the sense of improving classification performance and reducing the computational complexity of intrusion detection system. Feature reduction is even more important at online detection when less ...

This paper develops a new uncertainty measure for the theory of hints that complies with the established semantics of statistical information theory and further satisfies all classical requirements for such a measure imposed in the literature. The proposed functional decomposes into conversant uncertainty measures and therefore discloses a new interpretation of the latters as well. By abstracti...

The Dempster-Shafer theory of belief functions is an important approach to deal with uncertainty in AI. In the theory, belief functions are defined on Boolean algebras of events. In many applications of belief functions in real world problems, however, the objects that we manipulate is no more a Boolean algebra but a distributive lattice. In this paper, we extend the Dempster-Shafer theory to t...

This paper introduces the idea of using the Dempster-Shafer theory of evidence with qualitative values. Dempster-Shafer theory is a formalism for reasoning under uncertainty which may be viewed as a generalisation of probability theory with special advantages in its treatment of ambiguous data and the ignorance arising from it. Here we are interested in applying the theory when the numbers that...

The Dempster-Shafer theory of evidence accumulation is one of the main tools for combining data obtained from multiple sources. In this paper a special case of combination of two bodies of evidence with non-zero conflict coefficient is considered. It is shown that application of the Dempster-Shafer rule of combination in this case leads to an evaluation of masses of the combined bodies that is ...

This paper presents a clinical diagnosis support system which combines the advantages of Dempster-Shafer theory with Bayesian networks in order to simulate the uncertain medical reasoning. We propose a hierarchical structure using Dempster-Shafer at the upper level for evaluating more general hypothesis (disease groups) and Bayesian networks at the lower level for a more accurate analysis of sp...