Fusion Techniques for Reliable Information: A Survey <s> Dempster-Shafer Theory (DST) <s> The objective of this paper is to describe the potential offered 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 Artificial Intelligence (AI) and Expert Systems, with particular emphasis placed on combining evidence from different sources. In the paper we introduce the basic concepts of the DST of evidence, briefly mentioning its origins and comparisons with the more traditional Bayesian theory. Following this we discuss recent developments of this theory including analytical and application areas of interest. Finally we discuss developments via the use of an example incorporating DST with the Analytic Hierarchy Process (AHP). <s> BIB001 </s> Fusion Techniques for Reliable Information: A Survey <s> Dempster-Shafer Theory (DST) <s> 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 Context-aware Computing Project aims to build a generalizable sensor fusion architecture in a systematic way. This naturally leads us to choose the Dempster-Shafer approach as our first sensor fusion implementation algorithm. This paper discusses the relationship between Dempster-Shafer theory and the classical Bayesian method, describes our sensor fusion research work using Dempster-Shafer theory in comparison with the weighted sum of probability method. The experimental approach is to track a user’s focus of attention from multiple cues. Our experiments show promising, thought-provoking results encouraging further research. <s> BIB002 </s> Fusion Techniques for Reliable Information: A Survey <s> Dempster-Shafer Theory (DST) <s> The modeling of epitesmic knowledge is a necessity of most systems dealing with some sort of artificial reasoning. There are several formalisms able to mathematically model someone's degrees of belief. A very popular one is the Bayesian theory, which is based on a prior knowledge of a probability distribution. Another model is the theory of evidence, or Dempster-Shafer theory, which provides a method for combining evidences from different sources without prior knowledge of their distributions. In this latter method, it is possible to assign probability values to sets possibilities rather than to single events only, and it is not needed to divide all the probability values among the events, once the remaining probability should be assigned to the environment and not to the remaining events, thus modeling more naturally certain classes of problems. There are some pitfalls however, in particular, the Dempster-Shafer theory does not model well evidences with a high degree of conflict, and evidences with the more probable possibility disjoint but with a less probable possibility in common tend to bias the results toward the less probable hypothesis in an illogical way, assigning 100% probability of it. In this paper, we present an extension of Dempster-Shafer theory that overcome the aforementioned pitfalls, allowing the combination of evidences with higher degrees of conflict, and avoiding the excessive tendency toward the common possibility of otherwise disjoint hypothesis. This is accomplished by means of a new rule of evidences combination that embodies the conflict among the evidences, modeling naturally the epitesmic reasoning. <s> BIB003
DST is a generalization of Bayesian Theory (BT) of subjective probability. It allows us to base degrees of belief for one question on probabilities for the related question. How much they differ from the probabilities depend on how closely two questions are related BIB001 , BIB003 . DST is applied as a sensor fusion technique for managing uncertainty and for inferring mechanisms, which analogous to human reasoning process in BIB002 .