Social norms play a crucial role in guiding agents towards understanding and adhering to standards of behavior, thus reducing social conflicts within multi-agent systems (MASs). However, current LLM-based (or generative) MASs lack the capability to be normative. In this paper, we propose a novel architecture, named CRSEC, to empower the emergence of social norms within generative MASs. Our architecture consists of four modules: Creation & Representation, Spreading, Evaluation, and Compliance. This addresses several important aspects of the emergent processes all in one: (i) where social norms come from, (ii) how they are formally represented, (iii) how they spread through agents' communications and observations, (iv) how they are examined with a sanity check and synthesized in the long term, and (v) how they are incorporated into agents' planning and actions. Our experiments deployed in the Smallville sandbox game environment demonstrate the capability of our architecture to establish social norms and reduce social conflicts within generative MASs. The positive outcomes of our human evaluation, conducted with 30 evaluators, further affirm the effectiveness of our approach. Our project can be accessed via the following link: https://github.com/sxswz213/CRSEC.

翻译：社会规范在引导智能体理解并遵循行为标准、从而减少多智能体系统（MASs）中的社会冲突方面发挥着关键作用。然而，当前基于大语言模型（LLM）的（即生成式）MASs缺乏规范性能力。本文提出了一种名为CRSEC的新型架构，旨在赋能生成式MASs中社会规范的出现。我们的架构包含四个模块：创建与表征、传播、评估以及遵循。这一架构全面解决了涌现过程中的多个重要方面：（i）社会规范的来源，（ii）其形式化表征方式，（iii）如何通过智能体的通信与观察进行传播，（iv）如何通过合理性检查进行检验并在长期过程中进行综合，以及（v）如何将规范融入智能体的规划与行动。我们在Smallville沙盒游戏环境中开展的实验证明了该架构在生成式MASs中建立社会规范并减少社会冲突的能力。由30名评估者开展的人工评估得出的积极结果进一步证实了本方法的有效性。我们的项目可通过以下链接访问：https://github.com/sxswz213/CRSEC。