Adaptivity at every layer: a modular approach for evolving societies of learning autonomous systems

We describe a developmental architecture that enables individual robots to fulfill tasks assigned to the robot society in a robust, decentralized manner. The architecture is meant to show emergent properties according to Organic Computing principles that are positive for the society's robustness and performance. This requires the architecture to feature those adaptation and learning processes that are not only selfishly useful for the individual robot, but also incorporate the robot society's actual needs at all layers.