A distributed nanocluster based multi-agent evolutionary network

Liying Xu, Jiadi Zhu, Bing Chen, Zhen Yang, Keqin Liu, Bingjie Dang, Teng Zhang, Yuchao Yang () and Ru Huang ()
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Liying Xu: Peking University
Jiadi Zhu: Peking University
Bing Chen: Zhejiang University
Zhen Yang: Peking University
Keqin Liu: Peking University
Bingjie Dang: Peking University
Teng Zhang: Peking University
Yuchao Yang: Peking University
Ru Huang: Peking University

Nature Communications, 2022, vol. 13, issue 1, 1-10

Abstract: Abstract 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.

Date: 2022
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DOI: 10.1038/s41467-022-32497-5

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