This work presents a proof-theoretical and model-theoretical approach to probabilistic temporal logic . We present two novel logics; each of them extends both the language of linear time logic (LTL) and the language of probabilistic logic with polynomial weight formulas. The first logic is designed for reasoning about probabilities of temporal events, allowing statements like “the probability that A will hold in next moment is at least the probability that B will always hold” and conditional probability statements like “probability that A will always hold, given that B holds, is at least one half”, where A and B are arbitrary statements. We axiomatize this logic, provide corresponding sigma additive semantics and prove that the axiomatization is sound and strongly complete. We show that the satisfiability problem for our logic is decidable, by presenting a procedure which runs in polynomial space. We also present a logic with much richer language, in which probabilities are not attached only to temporal events, but the language allows arbitrary nesting of probability and temporal operators, allowing statements like “probability that tomorrow the chance of rain will be less than 80% is at least a half”. For this logic we prove a decidability result.

We present a proof-theoretical and model-theoretical approach to reasoning about knowledge and conditional probability. We extend both the language of epistemic logic and the language of linear weight formulas, allowing statements like “Agent Ag knows that the probability of A given B is at least a half”. We present both a propositional and a first-order version of the logic. We provide sound and complete axiomatizations for both logics and we prove decidability in the propositional case.

Abstract We introduce the probabilistic two-agent justification logic $\textsf {IPJ}$, a logic in which we can reason about agents that perform interactive proofs. In order to study the growth rate of the probabilities in $\textsf {IPJ}$, we present a new method of parametrizing $\textsf {IPJ}$ over certain negligible functions. Further, our approach leads to a new notion of zero-knowledge proofs.

Abstract In this paper, we present a first-order and a propositional logic for reasoning about degrees of confirmation. We define the appropriate formal languages and describe the corresponding classes of models. We provide infinitary axiomatizations for both logics and we prove that the axiomatizations are sound and strongly complete. We also show that our propositional logic is decidable. For some restrictions of the logics, we provide finitary axiomatic systems.

Abstract We present a probabilistic justification logic, $\mathsf{PPJ}$, as a framework for uncertain reasoning about rational belief, degrees of belief and justifications. We establish soundness and strong completeness for $\mathsf{PPJ}$ with respect to the class of so-called measurable Kripke-like models and show that the satisfiability problem is decidable. We discuss how $\mathsf{PPJ}$ provides insight into the well-known lottery paradox.

We present a first-order probabilistic epistemic logic, which allows combining operators of knowledge and probability within a group of possibly infinitely many agents. We define its syntax and semantics and prove the strong completeness property of the corresponding axiomatic system. 1

Abstract In this paper we provide a strongly complete axiomatization of a temporal epistemic logic in which non-rigid sets of agents are allowed. Using this framework, we prove a number of properties of the blockchain protocol with respect to the given set of axioms and premises.

Spatiotemporal databases can be used to efficiently store and retrieve information about objects moving in space and time. Probabilities are added to model the case where the locations are not known with certainty. A few years ago a new formalism was introduced to represent such information in the form of atomic formulas, each of which represents the probability (in the form of an interval because even the probabilities are not known precisely) that a particular object is in a particular location at a particular time. We extend this formalism to obtain several different probabilistic logics by adding logical operators. Furthermore, we axiomatize these logics, provide corresponding semantics, prove that the axiomatizations are sound and complete, and discuss decidability issues. While we relate these logics to previous axiomatizations of probabilistic logics, this article is self-contained: no prior knowledge of probabilistic logics is assumed.

We introduce a propositional and a first-order logic for reasoning about discrete linear time and finitely additive probability. The languages of these logics allow formulae that say ‘sometime in the future, α holds with probability at least s’. We restrict our study to so-called measurable models. We provide sound and complete infinitary axiomatizations for the logics. Furthermore, in the propositional case decidability is proved by establishing a periodicity argument for ω-sequences extending the decidability proof of standard propositional temporal logic LTL. Complexity issues are examined and a worst-case complexity upper bound is given. Extensions of the presented results and open problems are described in the final part of the paper.

We introduce new types of probability operators of the form QF, where F is a recursive rational subset of [0,1]. A formula QFα is satisfied in a probability model if the measure of the set of worlds that satisfy α is in F. The new operators are suitable for describing events in discrete sample spaces. We provide sound and complete axiomatic systems for a number of probability logics augmented with the QF-operators. We show that the new operators are not definable in languages of probability logics that have been used so far. We study decidability of the presented logics. We describe a relation of 'being more expressive' between the new probability logics. Key words: Probability logic, completeness, decidability.