Large Language Model Applied in Multi-agent SystemA Survey

Abstract. The application of large language models (LLMs) in single-agent systems within complex environments has proven successful, prompting a growing interest in their use within multi-agent systems (MAS). Despite the impressive capabilities of LLMs, it remains unclear how they can be optimally integrated and utilized to empower agents in MAS. Understanding how to effectively leverage the advantages of LLMs to enhance agent performance is crucial. This survey provides a comprehensive overview of the application of LLMs in MAS, focusing on their impact on agent cooperation, reasoning, and adaptive abilities. Finally, we discuss future directions and open questions in this evolving field.

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Publication Details

Journal
Applied and Computational Engineering
Published
2024-11-26
DOI
https://doi.org/10.54254/2755-2721/109/20241330
Citations
1
Primary Topic
Text and Document Classification Technologies
Type
article
Field-Weighted Citation Impact
0.35
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article

Large Language Model Applied in Multi-agent SystemA Survey

Kaiwen Dong
1 citations
Applied and Computational Engineering
Text and Document Classification Technologies
0.35
article

Large Language Model Applied in Multi-agent SystemA Survey

Kaiwen Dong
article en
1 citations

Abstract

Abstract. The application of large language models (LLMs) in single-agent systems within complex environments has proven successful, prompting a growing interest in their use within multi-agent systems (MAS). Despite the impressive capabilities of LLMs, it remains unclear how they can be optimally integrated and utilized to empower agents in MAS. Understanding how to effectively leverage the advantages of LLMs to enhance agent performance is crucial. This survey provides a comprehensive overview of the application of LLMs in MAS, focusing on their impact on agent cooperation, reasoning, and adaptive abilities. Finally, we discuss future directions and open questions in this evolving field.

Applied and Computational EngineeringVol. 109(1)
Lancaster University (GB)
Partnerships for the goals
Openalex Percentile: Top 31%
Text and Document Classification Technologies
0.35
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