LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplay
On the map: LLM agents and tool use → Social simulation and multi-agent debate
Abstract (arXiv)
This paper explores the open research problem of understanding the social behaviors of LLM-based agents. Using Avalon as a testbed, we employ system prompts to guide LLM agents in gameplay. While previous studies have touched on gameplay with LLM agents, research on their social behaviors is lacking. We propose a novel framework, tailored for Avalon, features a multi-agent system facilitating efficient communication and interaction. We evaluate its performance based on game success and analyze LLM agents' social behaviors. Results affirm the framework's effectiveness in creating adaptive agents and suggest LLM-based agents' potential in navigating dynamic social interactions. By examining collaboration and confrontation behaviors, we offer insights into this field's research and applications. Our code is publicly available at https://github.com/3DAgentWorld/LLM-Game-Agent.
Key facts. Cited by 62 papers in the map (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 24 of its 50 references that are in the map. First posted on arXiv 23 Oct 2023.
Builds on
24 of 50 references.
Closest ancestors · 2
Same direction, newest first
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