马尔可夫交换网络上带有一般线性系统的分布式多联盟游戏

IF 6.7 2区 计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY
Shuai Liu;Dong Wang;Zheng-Guang Wu
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引用次数: 0

摘要

本文研究了马尔可夫交换网络上具有不完全信息的多联盟博弈问题。参与该问题的每个异质博弈者都受一般线性动态驱动,并通过利用随机演化的网络来共享信息。所有参与者都希望最大限度地降低其联盟的成本函数,同时在联盟内部达成输出行动共识。为此,我们开发了一种基于比例积分动态共识协议的分布式多联盟博弈算法。利用图论、随机过程和稳定性原理,证明了该算法以指数方式收敛到纳什均衡解,并揭示了增益参数对收敛速度的影响。此外,出于对过渡概率无法获取的典型场景和限时任务需求的考虑,讨论还扩展到了具有部分未知过渡概率的马尔可夫交换网络和分布式预定义时间方案等情况。然后,分别给出了相应的分析结果。最后,通过数值模拟验证了所提算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Distributed Multi-Coalition Games With General Linear Systems Over Markovian Switching Networks
In this paper, the multi-coalition game problem with incomplete information over Markovian switching networks is investigated. Each heterogeneous player involved in the problem is driven by a general linear dynamic and shares information by exploiting the randomly evolving network. All players intend to minimize the cost function of their coalition while achieving output action consensus inside the coalition. Regarding this, we develop a distributed multi-coalition game algorithm based on the proportional-integral dynamic consensus protocol. With graph theory, stochastic processes, and the stability principle, the algorithm is proven to converge exponentially to the Nash equilibrium solution, and the effect of gain parameters on the convergence rate is revealed. In addition, motivated by concerns about typical scenarios in which transition probabilities are inaccessible and the demands of the time-limited task, the discussion is extended to cases including Markovian switching networks with partly unknown transition probabilities and the formulation of a distributed predefined time scheme. Then, the corresponding analytical results are given, respectively. Finally, the effectiveness of the proposed algorithm is verified by numerical simulations.
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来源期刊
IEEE Transactions on Network Science and Engineering
IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering
CiteScore
12.60
自引率
9.10%
发文量
393
期刊介绍: The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.
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