The Athenian Academy: A Seven-Layer Architecture Model for Multi-Agent Systems

• URL: http://arxiv.org/abs/2504.12735v2
• Date: Fri, 18 Apr 2025 02:45:06 GMT
• Title: The Athenian Academy: A Seven-Layer Architecture Model for Multi-Agent Systems
• Authors: Lidong Zhai, Zhijie Qiu, Lvyang Zhang, Jiaqi Li, Yi Wang, Wen Lu, Xizhong Guo, Ge Sun,
• Abstract summary: This paper proposes the "Academy of Athens" multi-agent seven-layer framework.<n>It addresses challenges in multi-agent systems (MAS) within artificial intelligence (AI) art creation.<n>The framework provides a structured methodology for multi-agent collaboration in AI art creation and promotes innovative applications in the art field.
• Score: 13.241259457317547
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper proposes the "Academy of Athens" multi-agent seven-layer framework, aimed at systematically addressing challenges in multi-agent systems (MAS) within artificial intelligence (AI) art creation, such as collaboration efficiency, role allocation, environmental adaptation, and task parallelism. The framework divides MAS into seven layers: multi-agent collaboration, single-agent multi-role playing, single-agent multi-scene traversal, single-agent multi-capability incarnation, different single agents using the same large model to achieve the same target agent, single-agent using different large models to achieve the same target agent, and multi-agent synthesis of the same target agent. Through experimental validation in art creation, the framework demonstrates its unique advantages in task collaboration, cross-scene adaptation, and model fusion. This paper further discusses current challenges such as collaboration mechanism optimization, model stability, and system security, proposing future exploration through technologies like meta-learning and federated learning. The framework provides a structured methodology for multi-agent collaboration in AI art creation and promotes innovative applications in the art field.

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