As multi-agent systems (MAS) become increasingly prevalent in autonomous systems, distributed control, and edge intelligence, efficient communication under resource constraints has emerged as a critical challenge. Traditional communication paradigms often emphasize message fidelity or bandwidth optimization, overlooking the task relevance of the exchanged information. In contrast, goal-oriented communication prioritizes the importance of information with respect to the agents' shared objectives. This review provides a comprehensive survey of goal-oriented communication in MAS, bridging perspectives from information theory, communication theory, and machine learning. We examine foundational concepts alongside learning-based approaches and emergent protocols. Special attention is given to coordination under communication constraints, as well as applications in domains such as swarm robotics, federated learning, and edge computing. The paper concludes with a discussion of open challenges and future research directions at the intersection of communication theory, machine learning, and multi-agent decision making.

翻译：随着多智能体系统在自主系统、分布式控制及边缘智能领域的日益普及，资源约束下的高效通信已成为关键挑战。传统通信范式通常强调消息保真度或带宽优化，却忽视了所交换信息与任务的相关性。与之相对，目标导向通信则优先考虑信息对智能体共享目标的重要性。本文对多智能体系统中的目标导向通信进行了全面综述，融合了信息论、通信理论与机器学习的视角。我们系统梳理了基础概念、基于学习的通信方法以及涌现协议，特别关注通信约束下的协作机制，以及在群体机器人、联邦学习与边缘计算等领域的应用。最后，本文探讨了通信理论、机器学习与多智能体决策交叉领域存在的开放性挑战与未来研究方向。