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

TL;DR

The paper introduces the Academy of Athens, a seven-layer MAS framework tailored for AI-driven art creation, drawing inspiration from The School of Athens to structure collaboration from perception to holistic synthesis. It details seven layers that address multi-agent collaboration, single-agent role flexibility, cross-scene adaptation, capability-centering avatars, and multi-model fusion, including a progression to multi-agent synthesis into a single target agent. Through diverse experiments—philosophical debates, cross-scene transitions, Da Vinci-inspired avatars, and cross-model collaborations—the framework demonstrates improvements in task coordination, cross-domain adaptation, and creative fusion, while also discussing challenges in collaboration optimization, model stability, scalability, and security. The authors suggest future directions such as meta-learning, AutoML, and federated learning to enhance robustness and applicability, positioning the framework as a structured blueprint for advancing AI-driven art creation and broader MAS applications.

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.

Figures (9)

• Figure 1: Raphael's The School of Athens
• Figure 2: Seven Definitions and Design Motivations of Multi-Agent Systems
• Figure 3: A Multi-Agent Collaborative Creation Framework Based on The School of Athens
• Figure 4: Philosophical Debate on the Integration of AI and Artistic Creation
• Figure 5: Teacher Edition, English Edition, Artist Edition Dialogue of Qiu Zhijie