Logics of knowledge and action: critical analysis and challenges

International audienceWe overview the most prominent logics of knowledge and action that were proposed and studied in the multiagent systems literature. We classify them according to these two dimensions, knowledge and action, and moreover introduce a distinction between individual knowledge and group knowledge, and between a nonstrategic an a strategic interpretation of action operators. For each of the logics in our classification we highlight problematic properties. They indicate weaknesses in the design of these logics and call into question their suitability to represent knowledge and reason about it. This leads to a list of research challenges

Doing Without Nature

We show that every indeterministic n-agent choice model Mi can be transformed into a deterministic n-agent choice model Md, such that Mi is a bounded morphic image of Md. This generalizes an earlier result from Van Benthem and Pacuit [16] about finite two-player choice models. It further strengthens the link between STIT logic and game theory, because deterministic choice models correspond in a straightforward way to normal game forms, and choice models are generally used to interpret STIT logic

From Classical to Non-monotonic Deontic Logic Using ASPIC+

In this paper we use formal argumentation to design non-monotonic deontic logics, based on two monotonic deontic logics. In particular, we use the structured argumentation theory ASPIC to define non-monotonic variants of well-understood modal logics. We illustrate the approach using argumentation about free-choice permission

Formalisation de systèmes d'agent cognitif, de la confiance et des émotions

International audienceA cognitive agent is an agent characterized by properties that are generally attributed to humans. Cognition is viewed here as a general mechanism of reasoning (in contrast with reactive agents) about knowledge. Such agents can perceive their environment, reason about fact or epistemic states of other agents, have a decision making process, etc. This article presents the main concepts used in cognitive agents formalizations, and speak about two particular concepts related to humans: trust and emotion. The language used for cognitive agents is here a logical language because it particularly fits well for both knowledge representation and reasoning formalization. But, even if trust and emotion can be both easily formalized by logical languages, we show that some numerical models are also well adapted