Benefiting from the powerful capabilities of large language models (LLMs), agents based on LLMs have shown the potential to address domain-specific tasks and emulate human behaviors. However, the content generated by these agents remains somewhat superficial, owing to their limited domain expertise and the absence of an effective cognitive architecture. To address this, we present the Configurable General Multi-Agent Interaction (CGMI) framework, designed to replicate human interactions in real-world scenarios. Specifically, we propose a tree-structured methodology for the assignment, detection, and maintenance of agent personality. Additionally, we designed a cognitive architecture equipped with a skill library based on the ACT* model, which contains memory, reflection, and planning modules. We have also integrated general agents to augment the virtual environment's realism. Using the CGMI framework, we simulated numerous classroom interactions between teacher and students. The experiments indicate that aspects such as the teaching methodology, curriculum, and student performance closely mirror real classroom settings. We will open source our work.

翻译：受益于大语言模型（LLMs）的强大能力，基于LLM的智能体已展现出解决领域特定任务和模拟人类行为的潜力。然而，由于这些智能体领域专业知识有限且缺乏有效的认知架构，其生成的内容仍较为浅显。为此，我们提出可配置通用多智能体交互（CGMI）框架，旨在复现真实场景中的人类交互行为。具体而言，我们提出了一种树状结构方法，用于智能体个性的分配、检测与维护。此外，我们设计了一个基于ACT*模型并配备技能库的认知架构，包含记忆、反思与规划模块。我们还集成了通用智能体以增强虚拟环境的真实感。利用CGMI框架，我们模拟了教师与学生之间的多组课堂互动。实验表明，教学方法、课程设置及学生表现等方面均高度贴近真实课堂场景。我们的工作将开源。