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Stochastic patrol routing is known to be advantageous in adversarialsettings; however, the optimal choice of stochastic routing strategy isdependent on a model of the adversary. We adopt a worst-case omniscientadversary model from the literature and extend the formulation to accommodateheterogeneous defenses at the various nodes of the graph. Introducing thisheterogeneity leads to interesting new patrol strategies. We identify efficientmethods for computing these strategies in certain classes of graphs. We assessthe effectiveness of these strategies via comparison to an upper bound on thevalue of the game. Finally, we leverage the heterogeneous defense formulationto develop novel defense placement algorithms that complement the patrolstrategies.

A Stochastic Surveillance Stackelberg Game: Co-Optimizing Defense Placement and Patrol Strategy