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Book

Lecture Notes in Artificial Intelligence

Vol. 9861: Belief Functions: Theory and Applications. Springer, 2016.
Academic editor: J. Vejnarová, V. Kratochvíl.
The theory of belief functions, also referred to as evidence theory or Dempster-Shafer theory, is a well-established general framework for reasoning with uncertainty. It has well-understood connections to other frameworks, such as probability, possibility, and imprecise probability theories. First introduced by Arthur P. Dempster in the context of statistical inference, the theory was later developed by Glenn Shafer into a general framework for modeling epistemic uncertainty. These early contributions have provided the starting points for many important developments, including the Transferable Belief Model and the Theory of Hints. The biennial BELIEF conferences (organized by the Belief Functions and Applications Society) are dedicated to the exchange of ideas, reporting of recent achievements, and presenting the wide range of applications of this theory. This conference series was started in Brest, France, in 2010; the second event was held in Compiègne, France, in May 2012; and the third in Oxford, UK, in September 2014. The present volume contains the proceedings of the 4th International Conference on Belief Functions, which took place in Prague, Czech Republic, on September 21–23, 2016. The book contains 25 peer-reviewed papers (out of a total number of 33 submissions) describing recent developments concerning both theoretical issues (including combination rules, conflict management, and generalized information theory) and applications in various areas (such as image processing, material sciences, and navigation).
Chapters
Lecture Notes in Artificial Intelligence