Formalization of recognition, affordances and learning in isolated or interacting animats

In this paper, we summarize the main properties of an algebra useful to describe architectures devoted to the control of autonomous and embodied “intelligent” systems. First, we use this formalism to propose a definition of perception as a potential function built from the integration of the sensori-motor signals. Next, we demonstrate the capability of a very simple architecture to learn to recognize and reproduce facial expressions without the innate capability to recognize the facial expressions of others. The solution relies on the importance of the interactions with another system/agent knowing already a set of emotional expressions. A condition for the learning stability of the proposed architecture is derived. The teacher agent must act as a mirror of the baby agent (and not as a classical teacher). As a result, the proposed architecture is able to learn the task. Finally, we discuss the limitations of the proposed formalism.