Multirobot Patrolling Against Adaptive Opponents with Limited Information

We study a patrolling problem where multiple agents are tasked with protecting an environment where one or more adversaries are trying to compromise targets of varying value. The objective of the patrollers is to move between targets to quickly spot when an attack is taking place and then diffuse it. Differently from most related literature, we do not assume that attackers have full knowledge of the strategies followed by the patrollers, but rather build a model at run time through repeated observations of how often they visit certain targets. We study three different solutions to this problem. The first two partition the environment using either a fast heuristic or an exact method that is significantly more time consuming. The third method, instead does not partition the environment, but rather lets every patroller roam over the entire environment. After having identified strengths and weaknesses of each method, we contrast their performances against attackers using different algorithms to decide whether to attack or not.