Learning from demonstration with swarm hierarchies
Keith M. Sullivan, Sean Luke · Adaptive Agents and Multi-Agents Systems · 2012
We present a supervised learning from demonstration system capable of training stateful and recurrent collective behaviors for multiple agents or robots. A model space of this kind is often high-dimensional and consequently may require a large number of samples to learn. Furthermore, the inverse problem posed by emergent macrophenomena among multiple agents presents major challenges to supervised learning methods. Our approach reduces the size of the state space, and shortens the gap between individual behaviors and macrophenomena, by manually decomposing individual behaviors and arranging the agents into a tree hierarchy. This makes it possible to train potentially large numbers of agents using a small number of samples. We demonstrate our system using hundreds of agents in a simulated foraging task, and on real robots performing a collective patrolling task.