Dempster-Shafer theory is one of the main tools for reasoning about data obtained from multiple sources, subject to uncertain information. In this work abstract algebraic properties of the Dempster-Shafer set of mass assignments are investigated and compared with the properties of the Bayes set of probabilities. The Bayes set is a special case of the Dempster-Shafer set, where all non-singleton...

In this paper, we consider an interesting issue related to artificial intelligence, namely artificial morality – i.e. can we perform ethical reasoning by computers? We introduce how to apply Bayesian networks, Dempster-Shafer theory and Assumption-based Truth Maintenance Systems for moral reasoning.

Context-sensing for context-aware HCI challenges the traditional sensor fusion methods with dynamic sensor configuration and measurement requirements commensurate with human perception. The Dempster-Shafer theory of evidence has uncertainty management and inference mechanisms analogous to our human reasoning process. Our Sensor Fusion for Contextaware Computing Project aims to build a generaliz...

Dempster-Shafer theory is widely applied in uncertainty modelling and knowledge reasoning due to its ability of expressing uncertain information. A distance between two basic probability assignments(BPAs) presents a measure of performance for identification algorithms based on the evidential theory of Dempster-Shafer. However, some conditions lead to limitations in practical application for Dem...

The framework for decision value oriented decomposition of data tables is stated with examples of its applications to partially generalized reasoning. Operation of synthesis of information is introduced for distributed decision tables. Theoretical foundations are built on the basis of the main factors of quality of reasoning, by referring to rough set, Dempster-Shafer and statistical theories.

Evidentia.l rea.. '>oning in ex pert systems has often used ad-hoc uncertainty calculi. Although it is generally accepted that probability theory provides a firm theoretical fo undat ion, researchers have found some problems with its usc as a workable uncertainty calculus. Among these problems arc representation ol' ignorance, consistency of probabilistic judgements, and adjustment of a priori ...

This paper presents an application of the Dempster-Shafer evidence combination scheme in building a rule based expert system shell for diagnostic reasoning. Domain knowledge is stored as rules with associated belief functions. The reasoning component uses a combination of forward and backward inferencing mechanisms to interact with the user in a mixed initiative format.

We are concerned with formal models of reasoning under uncertainty. Many approaches to this problem are known in the literature e.g. Dempster-Shafer theory, bayesian-based reasoning, belief networks, fuzzy logics etc. We propose rough mere-ology as a foundation for approximate reasoning about complex objects. Our notion of a complex object includes approximate proofs understood as schemes const...

This paper extends previous work on propagating qualitative uncertainty in networks in which a general approach to qualitative propagation was discussed. The work presented here includes results that make it possible to perform evidential and intercausal reasoning, in addition to the predictive reasoning already covered, in networks quantiied with probability, possibility and Dempster-Shafer be...

We describe a new approach for reasoning with belief functions in this paper. This approach is fundamentally unrelated to probabilities and is consistent with Shafer and Tversky's canonical examples.