Development via Information Self-structuring of Sensorimotor Experience and Interaction

Abstract. We describe how current work in Artificial Intelligence is using rigorous tools from information theory, namely information distance and experience distance to organize the self-structuring of sensorimotor perception, motor control, and experiential episodes with extended temporal horizon. Experience is operationalized from an embodied agent’s own perspective as the flow of values taken by its sensors and effectors (and possibly other internal variables) over a temporal window. Such methods allow an embodied agent to acquire the sensorimotor fields and control structure of its own body, and are being applied to pursue autonomous scaffolded proximal development in the zone between the familiar experience and the unknown. 1 Introduction: Information Self-structuring in Ontogeny Modern Artificial Intelligence (AI) research has increasingly focused on adaptive, embodied agents with rich sensing capabilities situated in complex environments, that develop in their capabilities over the course of their “lifetimes ” (ontogeny