In teams of humans and several robots, communication within the robot sub-group may occur without the human being necessarily aware of this, e.g., to update each other about task-relevant aspects of the environment. Nonetheless, since this affects their subsequent actions and visible behaviour, it has consequences for teamwork and the humans’ perception of the team, which are not always well-understood. Intra-robot communication and coordination can be beneficial, but may also be experienced negatively due to unexpected group dynamics.In this study, we designed three robot teams, with varying levels of shared learning: one where robots share information about the environment and other robots acknowledge receiving this information, one where this information is shared but receipt is not acknowledged, and one where there is no communication. In each case, robots assisted a human participant in repairing pipes in a simulated environment. We measured perceived entitativity, trust, and attribution of mind to the robots.Overall, our results illustrate that improving the skills of robots (in this case, shared learning) is not sufficient to also improve the human experience of being a member of such a team. How humans perceive artificial agents can be more important than their actual abilities. We thus explore implications for improving the human teammate’s understanding of robotic (social) abilities in hybrid teams.

Abstract The idealizations resulting from the use of Kripke semantics in Epistemic Logic are inherited by formalizations of group epistemic notions. For example, distributed knowledge is often taken to reflect the potential knowledge of a group: what agents would know if they had unbounded means of communication and deductive ability. However, this does not specify whether/how this potential can be actualized, especially since real people are not unbounded reasoners. Inspired by experiments on group reasoning, we identify two dimensions of actualizing distributed knowledge: communication and inference. We build a dynamic framework with effortful actions accounting for both, combining impossible-worlds semantics and action models, and we provide a method for extracting a sound and complete axiomatization.