The same technology that allows online advertisers to target consumers as we browse the web could eventually play a key role in communication network security – even at the national-defense level. An award-winning group of researchers published a study in IEEE Xplore saying that advertising algorithms--frequently used by search engines like Google and Yahoo--could help develop a machine-learning approach for navigating complex communication networks. In the past, it was relatively easy to both protect communication networks from attempted hacking and intercept (or jam) signals from other networks. These days, though, networks are anything but simple thanks to various factors such as mobile devices, sensing technologies, social networks, and others. As a result, understanding how these networks communicate is a challenge.

Operating vehicles in adversarial environments between a recurring origin-destination pair requires new planning techniques. Such a technique, presented in this paper, is a game inspired by Ruckle’s original contribution. The goal of the first player is to minimize the expected casualties undergone by a moving agent. The goal of the second player is to maximize this damage. The outcome of the game is obtained via a linear program that solves the corresponding minmax optimization problem over this outcome. The formulation originally proposed by Feron and Joseph is extended to different environment models in order to compute routing strategies over unstructured environments. To compare these methods for increasingly accurate representations of the environment, a grid-based model is chosen to represent the environment and the existence of a sufficient network size is highlighted. A global framework for the generation of realistic routing strategies between any two points is described. Finally the practicality of the proposed framework is illustrated on real world environments.