图形协调博弈中复杂且知情的对抗行为的影响
The Impact of Complex and Informed Adversarial Behavior in Graphical Coordination Games
AI总结：
本文研究图形协调博弈中对手策略复杂性与网络信息感知对系统性能的影响，发现最小连通环图最易受攻击，且复杂性与信息的相对重要性取决于对手影响力强弱。
摘要：
系统级信息如何影响对手在网络化控制系统中降低性能的能力？对手策略的复杂性又如何影响其降低性能的能力？本文在图形协调博弈的背景下关注这些问题，其中对手可以影响系统中给定比例的智能体，且智能体遵循对数线性学习——一种众所周知的分布式学习算法。针对一类具有不同连通性的同质环图，我们首先证明最小连通环图最易受到对抗影响。随后，我们刻画了对手如何利用（i）攻击策略的复杂程度（静态与动态）以及（ii）关于网络结构的信息感知来降低系统性能。针对诱导随机稳定状态的对抗策略集合，我们的发现表明，复杂性与信息之间的相对重要性会随着对手影响力的变化而变化。特别是，当对手的影响力相对较弱时，在降低系统级损害方面，复杂性远远超过信息感知。然而，当对手的影响力更强时，情况则相反。
英文摘要：
How does system-level information impact the ability of an adversary to degrade performance in a networked control system? How does the complexity of an adversary's strategy affect its ability to degrade performance? This paper focuses on these questions in the context of graphical coordination games where an adversary can influence a given fraction of the agents in the system, and the agents follow log-linear learning, a well-known distributed learning algorithm. Focusing on a class of homogeneous ring graphs of various connectivity, we begin by demonstrating that minimally connected ring graphs are the most susceptible to adversarial influence. We then proceed to characterize how both (i) the sophistication of the attack strategies (static vs dynamic) and (ii) the informational awareness about the network structure can be leveraged by an adversary to degrade system performance. Focusing on the set of adversarial policies that induce stochastically stable states, our findings demonstrate that the relative importance between sophistication and information changes depending on the the influencing power of the adversary. In particular, sophistication far outweighs informational awareness with regards to degrading system-level damage when the adversary's influence power is relatively weak. However, the opposite is true when an adversary's influence power is more substantial.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
