Author_Institution :
Inst. of Control Theory & Robotics, Slovak Acad. of Sci., Bratislava, Slovakia
Abstract :
Uncertainty is present in most tasks that require intelligent behaviour. Probability theory has by far the longest tradition in problems connected with uncertainty, but the Dempster-Shafer theory provides more general model. A central problem of this theory is conditioning. M. Spies´s paper (1994) presents a new approach to a solution of this problem, by establishing a link between conditional events and discrete random sets. Conditional events were introduced as sets of equivalent events under conditioning. These sets are targets of a multivalued mapping and conditional belief functions were introduced. We study properties of these functions in the cases that belief functions were obtained by Bayesian conditioning from an unconditional belief function
Keywords :
Bayes methods; belief maintenance; probability; uncertainty handling; Bayesian conditioning; Dempster-Shafer theory; belief functions; belief updating; conditional belief functions; conditioning; discrete random sets; intelligent behaviour; multivalued mapping; probability theory; uncertainty; unconditional belief function; Algebra; Bayesian methods; Control theory; Decision making; Random variables; Robots; Uncertainty;
Conference_Titel :
Uncertainty Modeling and Analysis, 1995, and Annual Conference of the North American Fuzzy Information Processing Society. Proceedings of ISUMA - NAFIPS '95., Third International Symposium on
Conference_Location :
College Park, MD
Print_ISBN :
0-8186-7126-2
DOI :
10.1109/ISUMA.1995.527782
