In this paper we deal with an approach to reasoning about numerical beliefs in a logical framework. Among the different models of numerical belief, probability theory is the most relevant. Nearly all logics of probability that have been proposed in the literature are based on classical two-valued logic. After making clear the differences between fuzzy logic and probability theory, that apply also to uncertainty measures in general, here we propose two different theories in a fuzzy logic to cope with probability and belief functions respectively. Completeness results are provided for them. The main idea behind this approach is that uncertainty measures of crisp propositions can be understood as truth-values of some suitable fuzzy propositions associated to the crisp ones.

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