Special Session Ⅵ

 

Game-Theoretic Decision-Making under Incomplete Information for Clustered Swarms in Dynamic Open Environments  
动态开放环境下基于不完全信息的分簇群智博弈决策    (Submission Deadline: September 11, 2026)

 

Chair:	Co-chairs:
Meng Luan Lingnan University, China	Xiao Fang Southeast University, China	Xuqiang Lei Southeast University, China	Guanghui Wen Southeast University, China
Keywords:
·  Swarm Decision-Making (集群决策) ·  Distributed Optimization (分布式优化) ·  Distributed Control (分布式控制) ·  Non-Cooperative Games (非合作博弈) ·  Incomplete Information (不完全信息)
Topics (include but are not limited to):
·  Distributed optimization and machine learning for large-scale swarms (大规模集群的分布式优化与机器学习) ·  Distributed adversarial decision-making and swarm game-theoretic learning (分布式对抗决策与集群博弈学习) ·  Multi-agent reinforcement learning and game-theoretic decision-making (多智能体强化学习与博弈决策) ·  Data-driven intelligent decision-making and cooperative control for swarms (数据驱动的无人集群智能决策与协同控制) ·  Embodied intelligence and its deep integration with swarm decision-making (具身智能及其与群体决策的深度融合) ·  Distributed learning-driven safety monitoring and cooperative control (分布式学习驱动的安全监测与协同控制) ·  Fault diagnosis and resilient fault-tolerant control in dynamic environments (复杂动态环境下的故障诊断与弹性容错控制) ·  Multi-agent opponent modeling and strategy adaptation in competitive environments (竞争环境下的多智能体对手建模与策略自适应) ·  Cooperative perception and information processing under uncertainty in complex environments (复杂环境下的协同感知与不确定性信息处理) ·  Task-driven multi-stage dynamic games and sequential decision-making for swarms (任务驱动的集群多阶段动态博弈与时序决策规划)
Summary:
·  Driven by the scalability limitations and resource constraints of heterogeneous swarms in complex missions, this session focuses on the frontiers of game-theoretic decision-making under incomplete information for clustered swarm systems in dynamic open environments. We will deeply explore how to achieve task-matching-based rapid alliance formation and optimal decision-making under highly dynamic conditions, while analyzing safe adaptive game mechanisms and multi-stage sequential planning for variable-dimensional clustered swarms, as well as distributed cooperative control strategies. Targeting scholars and industry practitioners across interdisciplinary fields such as systems control, artificial intelligence, robotics, and multi-agent systems, this session seeks to equip attendees with deep insights into the latest theoretical breakthroughs in swarm decision-making, ultimately providing cutting-edge methodologies for building efficient, safe and adaptive autonomous cooperative systems.
·  面向异构集群复杂任务中的规模时变与资源受限挑战，本专题聚焦动态开放环境下基于不完全信息的分簇群智博弈决策前沿。我们将深入探讨如何通过任务匹配驱动实现快速联盟构建与高动态最优决策，并剖析变维簇群的安全自适应博弈机制、多阶段时序规划框架，以及分布式协同控制策略等。本专题面向系统控制、人工智能、机器人与多智能体系统等交叉领域的学者及研发人员，旨在帮助参会者掌握群智决策的最新理论突破，为构建高效、安全、自适应的自主协同系统提供前沿方法论。