The role of uncertainty in interactive cognition

Agents interacting in dynamic environments face uncertainty about environmental states. This poses a fundamental challenge to cognition. In interactions with other agents, this uncertainty extends beyond sensor noise and changing environments to include the beliefs and preferences of those agents, which are not directly observable. Therefore, agents require an approach to model the uncertainty of these diverse beliefs and utilize them in cognition. This talk presents approaches to modeling uncertainty and explores their implications across physical, social, and introspective dimensions. From a physical perspective, quantifying uncertainty allows agents to evaluate potential actions by balancing pragmatic utility with epistemic information gain. This allows them to build a more accurate mental representation of their environment. Socially, modeling the unobservable beliefs and preferences of interaction partners fosters behavioral alignment and shared understanding. Introspectively, representing uncertainty regarding the agents goals and preferences can promote flexible and potentially safer behavior. To ground these theoretical concepts, we outline a planned application involving a humanoid robot in a dynamic factory setting. We will explore how integrating uncertainty modeling concepts could support the robot with real-time spatial reasoning, collaborative workflows, and safe task execution alongside human workers.