Abstract:In classic logic, premises, conclusions, and rules are treated deterministically, i.e., they are considered as either true or false. However, when dealing with reality, one finds that all these elements must be considered as uncertain. Thus classic logic must be extended to cover real situations. One possible extension is given by uncertainty measures together with aggregation formulas that combine the uncertainty of premises with that of the rules to obtain the uncertainty of conclusions. This paper describes different uncertainty measures, giving the physical meaning of the implied axioms and their limitations, illustrated by some examples. Finally, a classification of some of the well‐known uncertainty measures, such as belief and plausibility functions, probabilities, necessities, and possibilities, is give
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