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. We adopt a worst-case omniscient adversary model from the literature and extend the formulation to accommodate heterogeneous defenses at the various nodes of the graph. Introducing this heterogeneity leads to interesting new patrol strategies. We identify efficient methods for computing these strategies in certain classes of graphs. We assess the effectiveness of these strategies via comparison to an upper bound on the value of the game. Finally, we leverage the heterogeneous defense formulation to develop novel defense placement algorithms that complement the patrol strategies.

翻译：随机巡逻路径规划在对抗场景中已被证明具有优势；然而，最优随机巡逻策略的选择依赖于对对手的建模。我们采用文献中的最坏情况全知对手模型，并将该表述扩展至图结构中各节点可配备异构防御设施的场景。引入这种异质性催生了新颖有趣的巡逻策略。我们针对特定图类提出了计算这些策略的高效方法。通过与博弈价值的上界进行对比，评估了这些策略的有效性。最后，我们利用异构防御表述开发了与巡逻策略互补的新型防御部署算法。