Stochastic patrol routing is known to be advantageous in adversarial settings; however, the optimal choice of stochastic routing strategy is dependent on a model of the adversary. Duan et al. formulated a Stackelberg game for the worst-case scenario, i.e., a surveillance agent confronted with an omniscient attacker [IEEE TCNS, 8(2), 769-80, 2021]. In this article, we extend their formulation to accommodate heterogeneous defenses at the various nodes of the graph. We derive an upper bound on the value of the game. We identify methods for computing effective patrol strategies for certain classes of graphs. Finally, we leverage the heterogeneous defense formulation to develop novel defense placement algorithms that complement the patrol strategies.

翻译：随机巡逻路径在对抗场景中被认为具有优势，但最优随机巡逻策略的选择取决于对手模型。Duan等人针对最坏情形（即监控智能体面对全知攻击者）建立了斯塔克尔伯格博弈模型[IEEE TCNS, 8(2), 769-80, 2021]。本文将该模型扩展至图的各节点可配置异构防御的场景。我们推导了博弈值的上界，给出了特定图类中有效巡逻策略的计算方法，并最终利用异构防御模型设计了与巡逻策略互补的新型防御布局算法。