We present a propositional and a first-order logic for reasoning about higher-order upper and lower probabilities. We provide sound and complete axiomatizations for the logics and we prove decidability in the propositional case. Furthermore, we show that the introduced logics generalize some existing probability logics.

A propositional logic is defined which in addition to propositional language contains a list of probabilistic operators of the form P ≥s (with the intended meaning ‘‘the probability is at least s’’). The axioms and rules syntactically determine that ranges of probabilities in the corresponding models are always finite. The completeness theorem is proved. It is shown that completeness cannot be generalized to arbitrary theories.

We show how to obtain a probabilistic semantics and calculus for a logic presented by a valuation specification. By identifying general forms of valuation constraints we are able to accommodate a wide class of propositional based logics encompassing multi-valued logics like Łukasiewicz 3-valued logic and the Belnap–Dunn four-valued logic as well as paraconsistent logics like $${\textsf{mbC}}$$ and $${\textsf{LFI1}}$$. The probabilistic calculus is automatically generated from the valuation specification. Although not having explicit probability constructors in the language, the rules of the (...) calculus reflect the valuation constraints in a probabilistic way. Indeed the probability of the premises of each rule coincides with the probability of the conclusions. Moreover, a failed exhaustive attempt of proving a formula in this calculus means non-derivability. Nevertheless when the non-derived formula is consistent then it is possible to extract a satisfying valuation from the failed exhaustive attempt. Soundness and completeness of the calculi are established with respect to the probabilistic semantics consisting of probability spaces also induced by the valuation specification. Furthermore we prove the equivalence between the probabilistic and the valuation semantics. (shrink)