Abstract We introduce a novel semantics for a multi-agent epistemic operator of knowing how, based on an indistinguishability relation between plans. Our proposal is, arguably, closer to the standard presentation of knowing that modalities in classical epistemic logic. We study the relationship between this new semantics and previous approaches, showing that our setting is general enough to capture them. We also study the logical properties of the new semantics. First, we define a sound and complete axiomatization. Second, we define a suitable notion of bisimulation and prove correspondence theorems. Finally, we investigate the computational complexity of the model checking and satisfiability problems for the new logic.

Abstract This paper makes a first step towards a logic of learning from experiments. For this, we investigate formal frameworks for modeling the interaction of causal and (qualitative) epistemic reasoning. Crucial for our approach is the idea that the notion of an intervention can be used as a formal expression of a (real or hypothetical) experiment (Pearl, 2009, Causality. Models, Reasoning, and Inference, 2nd edn. Cambridge University Press, Cambridge; Woodward, 2003, Making Things Happen, vol. 114 of Oxford Studies in the Philosophy of Science. Oxford University Press). In a first step we extend a causal model (Briggs, 2012, Philosophical Studies, 160, 139–166; Galles and Pearl, 1998, An axiomatic characterisation of causal counterfactuals. Foundations of Science, 3, 151–182; Halpern, 2000, Axiomatizing causal reasoning. Journal of Artificial Intelligence Research, 12, 317–337; Pearl, 2009, Causality. Models, Reasoning, and Inference, 2nd edn. Cambridge University Press, Cambridge) with a simple Hintikka-style representation of the epistemic state of an agent. In the resulting setting, one can talk about the knowledge of an agent and information update. The resulting logic can model reasoning about thought experiments. However, it is unable to account for learning from experiments, which is clearly brought out by the fact that it validates the principle of no learning for interventions. Therefore, in a second step, we implement a more complex notion of knowledge (Nozick, 1981, Philosophical Explanations. Harvard University Press, Cambridge, Massachusetts) that allows an agent to observe (measure) certain variables when an experiment is carried out. This extended system does allow for learning from experiments. For all the proposed logics, we provide a sound and complete axiomatization.

The present work studies the expressivity hierarchy of ‘static’ and ‘dynamic’ languages over epistemic awareness models by characterizing each language's expressivity with its adequate notion of bisimulation. The studied ‘static’ languages are based on the operators for implicit and explicit knowledge, implicit and explicit possibility and awareness; the studied ‘dynamic’ languages extend the ‘static’ ones with operators that express the effect of the so-called epistemic awareness action models, structures that can represent changes in an agent's knowledge and awareness.

This article models collective decision making scenarios by using a priority-based aggregation procedure, the so-called lexicographic method, to represent a form of reliability-based ‘deliberation’. More precisely, it considers agents with a preference ordering over a set of objects and a reliability ordering over the agents themselves, providing a logical framework describing the way in which the public and simultaneous announcement of the individual preferences leads to individual preference upgrade. The main results are the definitions of this lexicographic upgrade for diverse types of reliability relations (in particular, the preorder and total preorder cases), a sound and complete axiom system for a language describing the effects of such upgrades, and the definitions for non-public variations.