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Special Session XI

 

Intelligent Decision-Making and Control for Multi-Agent Systems  (Submission Deadline: July 31, 2026)
多智能体系统的智能决策与控制 

 

Chair: Co-chairs:  
     

     
Xiuhui Peng 
Nanjing University of Aeronautics and Astronautics, China		 Peng Wang
Nanjing University of Aeronautics and Astronautics, China		 Tao Xu
Beijing Institute of Technology, China
     
Keywords:    
     
· Multi-Agent Systems (多智能体系统)
· Distributed Control (分布式控制)
· Multi-Agent Task Allocation (多智能体任务规划)
· Multi-Agent Reinforcement Learning (多智能体强化学习)
· Multi-Agent Collaborative Trajectory Planning (多智能体协同轨迹规划)
     
Topics:    
     
· Distributed optimization and coordination control for multi-agent systems (多智能体系统的分布式优化与协同控制)
· Decision-making and game theory in competitive and cooperative environments (竞争与合作环境下的决策与博弈论)
· Multi-Agent reinforcement learning and its convergence analysis (多智能体强化学习及其收敛性分析)
· Consensus control under communication constraints and intermittent links (通信受限及间歇链路下的共识控制)
· Security, privacy protection, and resilient control of multi-agent networks (多智能体网络的安全性、隐私保护与韧性控制)
· Collaborative task planning and pathfinding for large-scale swarms (大规模集群的协同任务规划与路径搜索)
· Applications in smart grids, intelligent transportation, and industrial IoT (在智能电网、智能交通及工业物联网中的应用)
     
Summary:    
     
· Multi-Agent systems represent a critical frontier in modern control science and artificial intelligence, offering robust solutions for complex, large-scale systems across diverse sectors. This special session focuses on the latest advancements in the intersection of decentralized control and intelligent decision-making. By integrating methods from consensus theory, multi-agent reinforcement learning, and distributed optimization, we aim to address the challenges of scalability, heterogeneity, and dynamic environments. We invite researchers and practitioners to submit original works that explore theoretical breakthroughs, novel algorithms, and real-world implementations, fostering a deeper understanding of how autonomous agents can interact to achieve global objectives efficiently and securely.
     

· 多智能体系统是现代控制科学与人工智能的前沿阵地，为各行业复杂的大规模系统提供了强有力的解决方案。本专题聚焦于分布式控制与智能决策交叉领域的最新进展。通过整合共识理论、多智能体强化学习和分布式优化等方法，旨在解决系统可扩展性、异构性以及动态环境带来的挑战。我们诚邀国内外相关领域的专家、学者及工程技术人员分享原创研究成果、创新算法及实际应用案例，共同探讨自主智能体如何通过交互高效、安全地实现全局目标。