Map learning with uninterpreted sensors and effectors
Artificial Intelligence
Polychronization: Computation with Spikes
Neural Computation
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Dimensionality reduction through sensory-motor coordination
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
AI in the 21st century - with historical reflections
50 years of artificial intelligence
Information dynamics of evolved agents
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
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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.