ABSTRACT
In the last decade, the problems being treated in Artificial Intelligence and Robotics have witnessed an increase in complexity as the domains under investigation have transitioned from theoretically clean scenarios to more complex dynamic environments. Agents that must adapt in environments such as the physical world, a factory floor, an active ecology or economy, and the World Wide Web, challenge traditional assumptions and approaches to learning. As a consequence, novel methods for automated adaptation, action selection, and new behavior acquisition have become the focus of much research in the field.
