Emergent behavior in swarm robotic systems is key to obtaining com- plex behavior by a group of relatively simple agents. The question is how to design the individual behaviors of agents in such a way that the desired global behavior emerges. Different approaches have been proposed to solve this problem: from bi- ologically inspired probabilistic behavioral models to evolutionary techniques. In some situations, however, creating a complex probabilistic model of the behavior or developing a proper setup for an evolutionary process can be challenging. In this paper we propose a new method, based on supervised learning on a relatively small number of training samples. We apply our method to the well-known clustering problem and show that this approach yields the desired global clustering behavior.
['Gregory Vorobyev', 'Andrew Vardy', 'Wolfgang Banzhaf']
Supervised Learning in Robotic Swarms: From Training Samples to Emergent Behavior