Effective social, team and multiagent behaviour requires that co-acting individuals or agents reciprocally coordinate and adjust their actions with respect to one another and to changing task demands. Pivotal to the structural organization of such behaviour is the ability of agents to effectively decide and communicate (signal) how, when and who should act, with robust decision-making and communication often differentiating expert from novice performance. This is true whether one considers the simple activity of two people moving a piece of furniture together, or the more complex activities that elite athletes engage in during team sports.

Due to the increasing presence of robotic and artificial agents within our social world, understanding and modelling the behavior of human actors during team behavior also has broad implications for the development of interactive artificial systems. Indeed, effective human-machine interaction relies on the ability of artificial agents to not only predict the future actions of human actors, but, in many instances, also enact human-like patterns of reciprocal behavior. This can be achieved by endowing artificial agents with the behavioral coordination, perceptual-motor, and decision-making policies employed by humans.