This paper describes a new approach that makes a robot learn by evaluating its own performance based on the use of its resources. For a behavior-based robot, this means that learning is accomplished from the observation of behavior use over time. The acquired knowledge can then be exploited for future selection of behaviors. When applied to the multi-robot domain, this approach will make the robot find regularities in its interactions with its environment and exploit them efficiently, eventually resulting in specialization within the group.
Mobile robotics, robot learning, behavior-based systems, learning from history, self-evaluation, multi-robot domain.