Distributed control increases system scalability, flexibility, and redundancy. Foundational to such decentralisation is consensus formation, by which decision-making and coordination are achieved. However, decentralised multi-agent systems are inherently vulnerable to disruption. To develop a resilient consensus approach, inspiration is taken from the study of social systems and their dynamics; specifically, the Deffuant Model. A dynamic algorithm is presented enabling efficient consensus to be reached with an unknown number of disruptors present within a multi-agent system. By inverting typical social tolerance, agents filter out extremist non-standard opinions that would drive them away from consensus. This approach allows distributed systems to deal with unknown disruptions, without knowledge of the network topology or the numbers and behaviours of the disruptors. A disruptor-agnostic algorithm is particularly suitable to real-world applications where this information is typically unknown. Faster and tighter convergence can be achieved across a range of scenarios with the social dynamics inspired algorithm, compared with standard Mean-Subsequence-Reduced-type methods.

翻译：分布式控制提升了系统的可扩展性、灵活性与冗余度。这种去中心化的基础在于共识形成，通过它实现决策制定与协同运作。然而，去中心化的多智能体系统本质上易受干扰。为构建弹性共识方法，本文从社会系统及其动力学研究中汲取灵感，具体而言，借鉴了Deffuant模型。我们提出一种动态算法，能够在多智能体系统中存在未知数量干扰源的情况下实现高效共识。通过反转典型的社会容忍度，智能体过滤掉那些会使其偏离共识的极端非标准观点。该方法使分布式系统在无需了解网络拓扑结构或干扰源数量与行为的前提下，即可应对未知干扰。这种不依赖干扰源的算法尤其适用于实际应用场景，其中此类信息通常未知。相较于标准均值-子序列-缩减类方法，基于社会动力学的启发式算法能在多种场景下实现更快、更紧密的收敛。