Multi-agent system application in accordance with game theory in bi-directional coordination network model  

作　　者：ZHANG Jie WANG Gang YUE Shaohua SONG Yafei LIU Jiayi YAO Xiaoqiang 

机构地区：[1]College of Electronics and Information Engineering,Air Force Engineering University,Xi’an 710054,China [2]College of Air Missile Defense,Air Force Engineering University,Xi’an 710054,China

出　　处：《Journal of Systems Engineering and Electronics》2020年第2期279-289,共11页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China(61503407,61806219,61703426,61876189,61703412);the China Postdoctoral Science Foundation(2016 M602996)。

摘　　要：The multi-agent system is the optimal solution to complex intelligent problems. In accordance with the game theory, the concept of loyalty is introduced to analyze the relationship between agents' individual income and global benefits and build the logical architecture of the multi-agent system. Besides, to verify the feasibility of the method, the cyclic neural network is optimized, the bi-directional coordination network is built as the training network for deep learning, and specific training scenes are simulated as the training background. After a certain number of training iterations, the model can learn simple strategies autonomously. Also,as the training time increases, the complexity of learning strategies rises gradually. Strategies such as obstacle avoidance, firepower distribution and collaborative cover are adopted to demonstrate the achievability of the model. The model is verified to be realizable by the examples of obstacle avoidance, fire distribution and cooperative cover. Under the same resource background, the model exhibits better convergence than other deep learning training networks, and it is not easy to fall into the local endless loop.Furthermore, the ability of the learning strategy is stronger than that of the training model based on rules, which is of great practical values.

关 键 词：LOYALTY GAME theory bi-directional COORDINATION network MULTI-AGENT system learning STRATEGY 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]