Resilient Consensus for Multi-Agent Systems Under Adversarial Spreading Processes
Resilient Consensus for Multi-Agent Systems Under Adversarial Spreading Processes
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DOI：
10.1109/tnse.2022.3176214
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发表时间：
2020-12
影响因子：
6.6
通讯作者：
Yuan Wang-;H. Ishii;François Bonnet;Xavier D'efago
中科院分区：
文献类型：
--
作者：
Yuan Wang-;H. Ishii;François Bonnet;Xavier D'efago
本文解决了在不可靠环境下运行的多智能体系统的共识问题。对抗性传播过程的动力学遵循易感-感染-恢复（SIR）模型，其中感染诱导agent的错误行为并影响其状态值。这种问题设置可以作为社会网络中意见动态的模型，在这种模型中，在大流行时要形成共识，受感染的个人可能会偏离他们的真实意见。为了确保非传染性因子之间有弹性的共识，困难在于传染性因子的数量随时间而变化。我们假设当地的政策制定者实时公布当地的感染水平，代理可以采用这些水平来采取预防措施。研究表明，在所谓的移动恶意模型存在的情况下，这个问题可以被表述为弹性共识，其中平均子序列减少（MSR）算法已知是有效的。我们描述了网络结构的充分条件，以适应有关宣布的感染水平和流行病强度的不同政策。对随机图进行了数值模拟，验证了该方法的有效性。
This paper addresses novel consensus problems for multi-agent systems operating in an unreliable environment where adversaries are spreading. The dynamics of the adversarial spreading processes follows the susceptible-infected-recovered (SIR) model, where the infection induces faulty behaviors in the agents and affects their state values. Such a problem setting serves as a model of opinion dynamics in social networks where consensus is to be formed at the time of pandemic and infected individuals may deviate from their true opinions. To ensure resilient consensus among the noninfectious agents, the difficulty is that the number of infectious agents changes over time. We assume that a local policy maker announces the local level of infection in real-time, which can be adopted by the agent for its preventative measures. It is demonstrated that this problem can be formulated as resilient consensus in the presence of the socalled mobile malicious models, where the mean subsequence reduced (MSR) algorithms are known to be effective. We characterize sufficient conditions on the network structures for different policies regarding the announced infection levels and the strength of the epidemic. Numerical simulations are carried out for random graphs to verify the effectiveness of our approach.