提出七层框架，让多个AI agent高效协作创作艺术
The Athenian Academy: A Seven-Layer Architecture Model for Multi-Agent Systems
- 分七层设计，明确各Agent角色与协作方式
- 实验验证可提升跨场景适应与多模型融合效果
- 适合研究AI艺术生成与多智能体系统者参考
本文提出面向AI艺术创作的多智能体系统（MAS）的“雅典学院”七层架构，旨在系统解决协作效率、角色分配、环境适应和任务并行等挑战。该框架将MAS分为七层：多智能体协作、单智能体多角色扮演、单智能体多场景遍历、单智能体多能力化身、不同单智能体使用同一大模型实现同一目标智能体、单智能体使用不同大模型实现同一目标智能体、多智能体合成同一目标智能体。在艺术创作实验中，该框架展现出任务协作、跨场景适应与模型融合的独特优势。文章还探讨了协作机制优化、模型稳定性与系统安全等当前挑战，建议未来通过元学习与联邦学习技术进一步探索。该框架为AI艺术创作中的多智能体协作提供了结构化方法，推动艺术领域创新应用。
原文摘要 · Abstract (English)
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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