Generative agents have demonstrated impressive capabilities in specific tasks, but most of these frameworks focus on independent tasks and lack attention to social interactions. We introduce a generative agent architecture called ITCMA-S, which includes a basic framework for individual agents and a framework called LTRHA that supports social interactions among multi-agents. This architecture enables agents to identify and filter out behaviors that are detrimental to social interactions, guiding them to choose more favorable actions. We designed a sandbox environment to simulate the natural evolution of social relationships among multiple identity-less agents for experimental evaluation. The results showed that ITCMA-S performed well on multiple evaluation indicators, demonstrating its ability to actively explore the environment, recognize new agents, and acquire new information through continuous actions and dialogue. Observations show that as agents establish connections with each other, they spontaneously form cliques with internal hierarchies around a selected leader and organize collective activities.

翻译：生成式智能体在特定任务中展现出令人印象深刻的能力，但现有框架大多聚焦于独立任务，缺乏对社会互动的关注。我们提出了一种名为ITCMA-S的生成式智能体架构，该架构包含个体智能体的基础框架以及支持多智能体间社会互动的LTRHA框架。该架构使智能体能够识别并过滤不利于社会互动的行为，引导其选择更有利的行动。我们设计了一个沙盒环境来模拟多个无身份智能体间社会关系的自然演化，以进行实验评估。结果表明，ITCMA-S在多项评估指标上表现良好，展现了其主动探索环境、识别新智能体以及通过持续行动与对话获取新信息的能力。观察发现，随着智能体间建立联系，它们会围绕选定的领导者自发形成具有内部层级的群体，并组织集体活动。