Limited security resources prevent full security coverage at all times, which allows adversaries to observe and exploit patterns in selective patrolling or mon itoring; for example, they can plan an attack avoiding existing pa trols. Hence, randomized patrolling or monitoring is impor tant, but randomization must provide distinct weights to dif ferent actions based on their complex costs and benefits. To this end, this article describes a promising transition of the lat est in multiagent algorithms into a deployed application. In particular, it describes a software assistant agent called AR MOR (assistant for randomized monitoring over routes) that casts this patrolling and monitoring problem as a Bayesian Stackelberg game, allowing the agent to appropriately weigh the different actions in randomization, as well as uncertainty over adversary types. ARMOR combines two key features.
Jan-4-2018, 12:20:44 GMT