Group-Based State Potential Games

被引:4
|
作者
Zhu, Rui [1 ]
Chen, Zengqiang [1 ]
Zhang, Zhipeng [2 ]
Yuan, Hongxing [1 ]
Liu, Zhongxin [1 ]
机构
[1] Nankai Univ, Coll Artificial Intelligence, Tianjin 300350, Peoples R China
[2] Tiangong Univ, Sch Artificial Intelligence, Tianjin 300387, Peoples R China
来源
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2023年 / 53卷 / 09期
基金
中国国家自然科学基金;
关键词
Games; Nash equilibrium; Optimization; Probabilistic logic; Multi-agent systems; Cybernetics; Computational complexity; Group-based game; recurrent state equilibrium; semi-tensor product (STP) of matrices; state potential game; EVOLUTIONARY GAME; DYNAMICS;
D O I
10.1109/TSMC.2023.3274135
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Facing different environments, i.e., states, there will be different game relationships among players, but there will also exist interactions among players to form a group. This article focuses on presenting the group-based state potential game (GSPG) and the strongly GSPG (SGSPG) to better describe such situations. Players who interact with each other can be regarded as a group and they will update strategies simultaneously. First, the concept of (strongly) GSPG is given and how to design two kinds of games is shown through the semi-tensor product (STP) of matrices. Subsequently, it is confirmed that their corresponding dynamics will converge almost surely to a strategy invariant set of (strongly) group-based recurrent state equilibriums and remain stable. Meanwhile, the equilibriums are acquired based on the algebraic forms of the games' dynamics, and the algorithm of the (strongly) GSPG process is established. Finally, the results in this article are demonstrated effectively by a typical example.
引用
收藏
页码:5638 / 5647
页数:10
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