As an important approach of distributed artificial intelligence, multi-agent system provides an efficient way to solve large-scale computational problems through high-parallelism processing with nonlinear interactions between the agents. However, the huge capacity and complex distribution of the individual agents make it difficult for efficient hardware construction. Here, we propose and demonstrate a multi-agent hardware system that deploys distributed Ag nanoclusters as physical agents and their electrochemical dissolution, growth and evolution dynamics under electric field for high-parallelism exploration of the solution space. The collaboration and competition between the Ag nanoclusters allow information to be effectively expressed and processed, which therefore replaces cumbrous exhaustive operations with self-organization of Ag physical network based on the positive feedback of information interaction, leading to significantly reduced computational complexity. The proposed multi-agent network can be scaled up with parallel and serial integration structures, and demonstrates efficient solution of graph and optimization problems. An artificial potential field with superimposed attractive/repulsive components and varied ion velocity is realized, showing gradient descent route planning with self-adaptive obstacle avoidance. This multi-agent network is expected to serve as a physics-empowered parallel computing hardware.

多智能体系统作为分布式人工智能的重要方法，通过智能体之间非线性交互的高并行处理，为解决大规模计算问题提供了一种有效的途径。但个体代理容量巨大、分布复杂，给高效的硬件建设带来困难。在这里，我们提出并演示了一种多智能体硬件系统，该系统部署分布式银纳米团簇作为物理智能体，及其在电场下的电化学溶解、生长和演化动力学，以实现解决方案空间的高并行性探索。银纳米团簇之间的协作和竞争使得信息能够得到有效的表达和处理，因此基于信息交互的正反馈的银物理网络的自组织取代了繁琐的详尽操作，从而显着降低了计算复杂度。所提出的多智能体网络可以通过并行和串行集成结构进行扩展，并展示了图和优化问题的有效解决方案。实现了具有叠加吸引/排斥分量和变化离子速度的人工势场，显示了具有自适应避障功能的梯度下降路线规划。这种多智能体网络有望作为物理驱动的并行计算硬件。