Abstract.Current dynamic-epistemic logics model different types of information change in multi-agent scenarios. We generalize these logics to a probabilistic setting, obtaining a calculus for multi-agent update with three natural slots: prior probability on states, occurrence probabilities in the relevant process taking place, and observation probabilities of events. To match this update mechanism, we present a complete dynamic logic of information change with a probabilistic character. The completeness proof follows a compositional methodology that applies to a much larger class of dynamic-probabilistic logics as well. Finally, we discuss how our basic update rule can be parameterized for different update policies, or learning methods.
We present a logic for reasoning both about the ability of agents to cooperate to execute complex actions, and how this relates to their ability to reach certain states of affairs. We show how the logic can be obtained in a modularised way, by combining a model for reasoning about actions and their effects with a model that describes what actions an agent can perform. More precisely, we show how one can combine an action logic which resembles Propositional Dynamic Logic with a cooperation logic which resembles Coalition Logic. We give a sound and complete axiomatisation for the logic, illustrate its use by means of an example, and discuss possible future extensions to it.
We examine the paradox of the surprise examination using dynamic epistemic logic. This logic contains means of expressing epistemic facts as well as the effects of learning new facts, and is therefore a natural framework for representing the puzzle. We discuss a number of different interpretations of the puzzle in this context, and show how the failure of principle of success, that states that sentences, when learned, remain to be true and come to be believed, plays a central role in understanding the puzzle.
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