首页|Semantic Information Extraction and Multi-Agent Communication Optimization Based on Generative Pre-Trained Transformer

Semantic Information Extraction and Multi-Agent Communication Optimization Based on Generative Pre-Trained Transformer

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  • NETL
  • NSTL
  • IEEE
The collaboration among multiple agents demands for efficient communication. However, the observational data in the multi-agent systems are typically voluminous and redundant and pose substantial challenges to the communication system when transmitted directly. To address this issue, this paper introduces a multi-agent communication scheme based on large language model (LLM), referred to as GPT-based semantic information extraction for multi-agent communication (GMAC). This scheme utilizes an LLM to extract semantic information and leverages the generative capabilities to predict subsequent actions, thereby enabling agents to make more informed decisions. The GMAC approach significantly reduces signaling expenditure exchanged among agents by extracting key semantic data via LLM. This method not only simplifies the communication process but also effectively reduces the communication overhead by approximately 53% compared to the baseline methods. Experimental results indicate that GMAC not only improves the convergence speed and accuracy of decision-making but also substantially decreases the signaling expenditure among agents. Consequently, GMAC offers a straightforward and effective method to achieve efficient and economical communication in the multi-agent systems.

BandwidthData miningData modelsTrainingData communicationComputational modelingMulti-agent systemsLarge language modelsGenerative adversarial networksFeature extraction

Li Zhou、Xinfeng Deng、Zhe Wang、Xiaoying Zhang、Yanjie Dong、Xiping Hu、Zhaolong Ning、Jibo Wei

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College of Electronic Science and Technology, National University of Defense Technology, Changsha, China

Artificial Intelligence Research Institute and the Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing, Shenzhen MSU-BIT University, Shenzhen, China

Artificial Intelligence Research Institute and the Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing, Shenzhen MSU-BIT University, Shenzhen, China|School of Medical Technology, Beijing Institute of Technology, Beijing, China

School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China

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2025

10.1109/TCCN.2024.3482354

IEEE transactions on cognitive communications and networking

IEEE transactions on cognitive communications and networking

ISSN：
年,卷(期)：2025.11(2)
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