With the rise of large language models (LLMs), AI agents as autonomous decision-makers present significant opportunities and challenges for human-AI cooperation. While many studies have explored human cooperation with AI as tools, the role of LLM-augmented autonomous agents in competitive-cooperative interactions remains under-examined. This study investigates human cooperative behavior by engaging 30 participants who interacted with LLM agents exhibiting different characteristics (purported human, purported rule-based AI agent, and LLM agent) in repeated Prisoner's Dilemma games. Findings show significant differences in cooperative behavior based on the agents' purported characteristics and the interaction effect of participants' genders and purported characteristics. We also analyzed human response patterns, including game completion time, proactive favorable behavior, and acceptance of repair efforts. These insights offer a new perspective on human interactions with LLM agents in competitive cooperation contexts, such as virtual avatars or future physical entities. The study underscores the importance of understanding human biases toward AI agents and how observed behaviors can influence future human-AI cooperation dynamics.

本研究针对人类与大型语言模型（LLM）增强的自主代理在竞争合作互动中的合作行为进行了探索，填补了这一研究领域的空白。通过对30名参与者在囚徒困境游戏中的互动分析，结果显示不同代理特征显著影响人类的合作行为，并揭示了性别及代理特征的交互作用。此研究为理解人类与LLM代理的互动提供了新视角，有助于优化未来人机合作的动态。

实验探索：研究人类与大型语言模型代理之间的合作互动行为