A market-based approach to planning in area surveillance

Abstract

Area surveillance is the problem of continuously monitoring a given area for intruders or for unexpected events. Recent work has focused on the use of autonomous teams of agents for surveillance, which creates a significant planning problem. We describe an algorithm for planning in area surveillance that uses the recently-developed evolutionary optimisation technique of market-based programming, where agents develop good surveillance plans by trading tasks between them according to self-interested free-market principles. This approach is robust and scalable and it deals well with heterogeneous and dynamic environments. Experiments show that our market-based algorithm can generate good solutions to the area surveillance problem.