Artificial intelligence-empowered smart things (e.g., robots, autonomous vehicles, and unmanned aerial vehicles) have been transforming the world. The “brains” of smart things can be abstracted as the agents or cybertwins residing on end devices and edge servers. The next-generation communication networks (i.e., 6G) will become the nervous system for these agents and natively support multiagent cooperation. By sharing local observations and intentions via communication channels, the agents could better understand the environments and make right decisions. Due to the limited channel bandwidth, the communication is considered as a bottleneck of multiagent cooperation. In this article, we propose a graph convolutional communication method (GraphComm) for multiagent cooperation to relive the bottleneck. Specifically, a variational information bottleneck is used to encode the observations and intentions compactly. Furthermore, a graph information bottleneck with the attention-based neighbor sampling mechanism is utilized to improve the effectiveness and robustness of the multiround communication process. The experimental results show that GraphComm can improve the effectiveness, robustness, and efficiency of communication in multiagent cooperative tasks as compared to baseline methods.

中文翻译：

---

GraphComm：用于多智能体合作的高效图卷积通信

人工智能赋能的智能事物（例如机器人、自动驾驶汽车和无人机）一直在改变世界。智能事物的“大脑”可以抽象为驻留在终端设备和边缘服务器上的代理或网络孪生。下一代通信网络（即 6G）将成为这些代理的神经系统，并在本地支持多代理合作。通过沟通渠道分享当地的观察和意图，代理可以更好地了解环境并做出正确的决定。由于信道带宽有限，通信被认为是多智能体合作的瓶颈。在本文中，我们提出了一种用于多智能体合作的图卷积通信方法（GraphComm）来重温瓶颈。具体来说，变分信息瓶颈用于对观察和意图进行紧凑编码。此外，利用基于注意力的邻居采样机制的图信息瓶颈来提高多轮通信过程的有效性和鲁棒性。实验结果表明，与基线方法相比，GraphComm 可以提高多智能体协作任务中通信的有效性、鲁棒性和效率。