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：得益于大型语言模型（LLMs）的强大能力，基于LLMs的智能体已展现出解决特定领域任务和模拟人类行为的潜力。然而，由于这些智能体领域知识有限且缺乏有效的认知架构，其生成的内容仍显浅层。为此，我们提出了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。具体而言，我们提出了一种树形结构方法来分配、检测和维护智能体个性；同时基于ACT*模型设计了配备技能库的认知架构，该架构包含记忆、反思和规划模块。此外，我们还整合了通用智能体以增强虚拟环境的逼真度。通过CGMI框架，我们模拟了师生之间的多轮课堂交互。实验表明，教学方法、课程内容和学生表现等方面均高度贴近真实课堂环境。我们将开源本项工作。