Inference by aggregation of evidence with applications to fuzzy probabilities

Optimal aggregation plays an important role towards developing a theory of aggregation of fuzzy concepts, based on non-additive set-functions and nonlinear (Choquet or fuzzy) integrals. This paper presents a framework supporting such aggregations and argues that such a framework is useful to decision making based on distributed sources of evidence.