This work develops an approach to qualitative belief revision in a fully probabilistic setting. We begin with a logic where possible worlds are assigned probabilities. In this logic an agent may believe a formula is true even though the subjective probability of the formula is less than 1.0. Similarly, after revision by a formula ϕ, the agent will believe ϕ is true, even though the agent’s subjective probability of ϕ may be less than 1.0. We establish a correspondence with the hallmark AGM postulates for belief revision. Moreover, we use Jeffrey Conditionalisation to establish a link with iterated belief change. To this end, we develop an approach that satisfies appropriately modified Darwiche-Pearl postulates (with clear justification). Thus, we provide a connection between quantitative probabilistic approaches on the one hand and the qualitative formulation of belief change, on the other. This work holds potential for the development of practical belief revision systems by applying a (qualitative) approach to belief change in probabilistic, uncertain domains.

A major challenge in AI is dealing with uncertain information. While probabilistic approaches have been employed to address this issue, in many situations probabilities may not be available or may be unsuitable. As an alternative, qualitative approaches have been introduced to express that one event is no more probable than another. We provide an approach where an agent may reason deductively about notions of likelihood, and may hold beliefs where the subjective probability for a belief is less than 1. Thus, an agent can believe that p holds (with probability <1); and if the agent believes that q is more likely than p, then the agent will also believe q. Our language allows for arbitrary nesting of beliefs and qualitative likelihoods. We provide a sound and complete proof system for the logic with respect to an underlying probabilistic semantics, and show that the language is equivalent to a sublanguage with no nested modalities.

Abstract This article introduces a Probabilistic Logic of Communication and Change , which captures in a unified framework subjective probability, arbitrary levels of mutual knowledge and a mechanism for multi-agent Bayesian updates that can model complex social-epistemic scenarios, such as informational cascades. We show soundness, completeness and decidability of our logic, and apply it to a concrete example of cascade.

This paper presents a sound axiomatization for a probabilistic modal dynamic logic of quantum programs. The logic can express whether a state is separable or entangled, information that is local to a subsystem of the whole quantum system, and the probability of positive answers to quantum tests of certain properties. The power of this axiomatization is demonstrated with proofs of properties concerning bases of a finite-dimensional Hilbert space, composite systems, entangled and separable states, and with proofs of the correctness of two probabilistic quantum protocols (the quantum leader election protocol and the BB84 quantum key distribution protocol).

The concurrency theory literature offers a wealth of examples of characteristic-formula constructions for various behavioural relations over finite labelled transition systems and Kripke structures that are defined in terms of fixed points of suitable functions. Such constructions and their proofs of correctness have been developed independently, but have a common underlying structure. This paper provides a general view of characteristic formulae that are expressed in terms of logics that have a facility for the recursive definition of formulae. We show how several examples of characteristic-formula constructions in the literature can be recovered as instances of the proposed general framework, and how the framework can be used to yield novel constructions. The paper also offers general results pertaining to the definition of co-characteristic formulae and of characteristic formulae expressed in terms of infinitary modal logics.

This paper extends probabilistic dynamic epistemic logic from a finite setting to an infinite setting, by introducing σ-algebras to the probability spaces in the models. This may extend the applicability of the logic to a real world setting with infinitely many possible measurements. It is shown that the dynamics preserves desirable properties of measurability and that completeness of the proof system holds with the extended semantics.