  This study investigated how social interaction among robotic agents changes
dynamically depending on the individual belief of action intention. In a set of
simulation studies, we examine dyadic imitative interactions of robots using a
variational recurrent neural network model. The model is based on the free
energy principle such that a pair of interacting robots find themselves in a
loop, attempting to predict and infer each other's actions using active
inference. We examined how regulating the complexity term to minimize free
energy determines the dynamic characteristics of networks and interactions.
When one robot trained with tighter regulation and another trained with looser
regulation interact, the latter tends to lead the interaction by exerting
stronger action intention, while the former tends to follow by adapting to its
observations. The study confirms that the dyadic imitative interaction becomes
successful by achieving a high synchronization rate when a leader and a
follower are determined by developing action intentions with strong belief and
weak belief, respectively.
