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：已知在对抗环境中，随机巡逻路径具有优势；然而，最优随机巡逻策略的选择取决于对对手行为的建模。本文采用文献中的最坏情况全知对手模型，并将该框架扩展至可容纳图中各节点的异构防御部署。引入这种异构性将催生出新颖有趣的巡逻策略。我们针对特定图类提出了这些策略的有效计算方法，并通过与博弈价值上界的对比评估其有效性。最后，我们利用异构防御模型框架，开发出与巡逻策略互补的新型防御部署算法。