This paper aims to investigate the open research problem of uncovering the social behaviors of LLM-based agents. To achieve this goal, we adopt Avalon, a representative communication game, as the environment and use system prompts to guide LLM agents to play the game. While previous studies have conducted preliminary investigations into gameplay with LLM agents, there lacks research on their social behaviors. In this paper, we present a novel framework designed to seamlessly adapt to Avalon gameplay. The core of our proposed framework is a multi-agent system that enables efficient communication and interaction among agents. We evaluate the performance of our framework based on metrics from two perspectives: winning the game and analyzing the social behaviors of LLM agents. Our results demonstrate the effectiveness of our framework in generating adaptive and intelligent agents and highlight the potential of LLM-based agents in addressing the challenges associated with dynamic social environment interaction. By analyzing the social behaviors of LLM agents from the aspects of both collaboration and confrontation, we provide insights into the research and applications of this domain. Our code is publicly available at https://github.com/3DAgentWorld/LLM-Game-Agent

翻译：本文旨在探究基于大语言模型（LLM）的智能体所展现的社会行为这一开放研究问题。为实现此目标，我们采用具有代表性的通讯游戏阿瓦隆作为研究环境，并通过系统提示词引导LLM智能体进行游戏。尽管已有研究初步探索了LLM智能体的游戏过程，但对其社会行为的研究仍属空白。本文提出一种能够无缝适配阿瓦隆游戏机制的新型框架，其核心是一个支持智能体间高效通讯与交互的多智能体系统。我们从获胜能力与智能体社会行为分析两个维度对所提框架进行性能评估。实验结果验证了该框架在生成具备适应性与自主性的智能体方面的有效性，揭示了基于LLM的智能体在应对动态社会环境交互挑战中的潜力。通过分别从合作与对抗维度分析LLM智能体的社会行为，我们为该领域的研究与应用提供了重要启示。相关代码已开源发布于 https://github.com/3DAgentWorld/LLM-Game-Agent