Can we avoid wars at the crossroads of history? This question has been pursued by individuals, scholars, policymakers, and organizations throughout human history. In this research, we attempt to answer the question based on the recent advances of Artificial Intelligence (AI) and Large Language Models (LLMs). We propose \textbf{WarAgent}, an LLM-powered multi-agent AI system, to simulate the participating countries, their decisions, and the consequences, in historical international conflicts, including the World War I (WWI), the World War II (WWII), and the Warring States Period (WSP) in Ancient China. By evaluating the simulation effectiveness, we examine the advancements and limitations of cutting-edge AI systems' abilities in studying complex collective human behaviors such as international conflicts under diverse settings. In these simulations, the emergent interactions among agents also offer a novel perspective for examining the triggers and conditions that lead to war. Our findings offer data-driven and AI-augmented insights that can redefine how we approach conflict resolution and peacekeeping strategies. The implications stretch beyond historical analysis, offering a blueprint for using AI to understand human history and possibly prevent future international conflicts. Code and data are available at \url{https://github.com/agiresearch/WarAgent}.

通过使用人工智能和大型语言模型，在历史的国际冲突中，包括第一次世界大战（WWI）、第二次世界大战（WWII）和中国的战国时期（WSP），我们提出了一种名为WarAgent的多智能体人工智能系统，模拟参与国家、它们的决策以及结果。通过评估模拟的效果，我们研究了先进人工智能系统在研究复杂的集体人类行为（如国际冲突）方面的进展和限制。我们的研究结果提供了以数据驱动和人工智能增强的洞察力，重新定义我们解决冲突和维持和平策略的方法。这些发现的意义超出了历史分析，为使用人工智能理解人类历史并可能预防未来的国际冲突提供了蓝图。

战争与和平（战争代理）：基于大语言模型的世界大战多智能体模拟