Simulations of dynamical interactions for social learning (original) (raw)

Often viewed as a tool for learning, imitation also has a communication purpose. In this paper we consider the interactional side of imitation, and especially its dynamic. We study simulations of interactions between two agents. We show, how improvements of an architecture designed for learning by imitation, permits to have a stable interaction: a synchronization of both agents. We also show that the dynamic of the interactions provides useful informations to build a reinforcement signal. That can be used to learn an arbitrary set of perception-action associations