Emergence of Social Norms in Generative Agent Societies: Principles and Architecture

Emergence of Social Norms in Generative Agent Societies: Principles and Architecture

Siyue Ren, Zhiyao Cui, Ruiqi Song, Zhen Wang, Shuyue Hu

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Human-Centred AI. Pages 7895-7903. https://doi.org/10.24963/ijcai.2024/874

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.
Keywords:
Agent-based and Multi-agent Systems: MAS: Normative systems
Agent-based and Multi-agent Systems: MAS: Agent-based simulation and emergence
Agent-based and Multi-agent Systems: MAS: Agent societies
Machine Learning: ML: Applications