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arXiv 2403.09009eess.SYcs.SY
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一种考虑状态不精确性和对抗性智能体的弹性分布式共识的几何方法

A Geometric Approach to Resilient Distributed Consensus Accounting for State Imprecision and Adversarial Agents

Christopher A. Lee, Waseem Abbas

AI总结:

本文提出一种几何方法,通过将智能体状态建模为不精确区域并计算不变包与安全点,实现对抗性智能体存在下的弹性分布式共识,兼顾状态不精确性与鲁棒性。

摘要:

本文提出了一种新颖的方法,用于在多智能体网络中实现弹性分布式共识,以应对对抗性智能体以及正常智能体所观察到的状态的不精确性。传统的弹性分布式共识算法通常假设智能体对其邻居的状态有精确的了解,这在实际场景中是不现实的。我们表明,当智能体只能获得邻居的不精确状态时,这些现有方法是不充分的。为了克服这一挑战,我们采用了一种几何方法,将智能体的状态建模为一个“不精确区域”,而不是$\mathbb{R}^d$中的一个点。从给定的一组不精确区域出发,我们首先提出了一种高效的方法来计算一个区域,该区域保证位于智能体真实(尽管未知)状态的凸包内。我们将该区域称为不精确区域的“不变包”,并提供其几何特征描述。接下来,我们使用这些不变包来为每个正常智能体识别一个“安全点”。智能体的安全点位于其正常邻居状态的凸包内,因此该智能体使用它来更新其状态。这导致正常智能体的状态聚合到其初始状态凸包内的安全点,或实现共识的近似。我们还通过仿真展示了我们的结果。我们的贡献通过适应状态不精确性来增强弹性分布式共识算法的鲁棒性,同时不损害对对抗性智能体的弹性。

英文摘要:

This paper presents a novel approach for resilient distributed consensus in multiagent networks when dealing with adversarial agents imprecision in states observed by normal agents. Traditional resilient distributed consensus algorithms often presume that agents have exact knowledge of their neighbors' states, which is unrealistic in practical scenarios. We show that such existing methods are inadequate when agents only have access to imprecise states of their neighbors. To overcome this challenge, we adapt a geometric approach and model an agent's state by an `imprecision region' rather than a point in $\mathbb{R}^d$. From a given set of imprecision regions, we first present an efficient way to compute a region that is guaranteed to lie in the convex hull of true, albeit unknown, states of agents. We call this region the \emph{invariant hull} of imprecision regions and provide its geometric characterization. Next, we use these invariant hulls to identify a \emph{safe point} for each normal agent. The safe point of an agent lies within the convex hull of its \emph{normal} neighbors' states and hence is used by the agent to update it's state. This leads to the aggregation of normal agents' states to safe points inside the convex hull of their initial states, or an approximation of consensus. We also illustrate our results through simulations. Our contributions enhance the robustness of resilient distributed consensus algorithms by accommodating state imprecision without compromising resilience against adversarial agents.

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。

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