Opportunistically cooperative neural learning in mobile agents

Searching a spatially extended environment using autonomous mobile agents is a problem that arises in many applications, e.g., search-and-rescue, search-and-destroy, intelligence gathering, surveillance, disaster response, exploration, etc. Since agents such as UAV's are often energy-limited and operate in a hostile environment, there is a premium on efficient cooperative search without superfluous communication. In this paper, we consider how a group of mobile agents, using only limited messages and incomplete information, can learn to search an environment efficiently. In particular, we consider the issue of centralized vs. decentralized intelligence and the effect of opportunistic sharing of learned information on search performance.