Supervised self-organization of large homogeneous Swarms using Ergodic Projections of Markov Chains

This paper formulates a self-organization algorithm to addresses the problem of emergent behavior supervision in engineered swarms of arbitrary population size. Based on collections of independent identical finite-state agents, the algorithm is derived to compute necessary perturbations in the local agents´ behavior, which guarantees convergence to the desired observed state of the swarm. A simulation example illustrates the underlying concept.