This paper presents BattleAgent, an emulation system that combines the Large
Vision-Language Model and Multi-agent System. This novel system aims to
simulate complex dynamic interactions among multiple agents, as well as between
agents and their environments, over a period of time. It emulates both the
decision-making processes of leaders and the viewpoints of ordinary
participants, such as soldiers. The emulation showcases the current
capabilities of agents, featuring fine-grained multi-modal interactions between
agents and landscapes. It develops customizable agent structures to meet
specific situational requirements, for example, a variety of battle-related
activities like scouting and trench digging. These components collaborate to
recreate historical events in a lively and comprehensive manner while offering
insights into the thoughts and feelings of individuals from diverse viewpoints.
The technological foundations of BattleAgent establish detailed and immersive
settings for historical battles, enabling individual agents to partake in,
observe, and dynamically respond to evolving battle scenarios. This methodology
holds the potential to substantially deepen our understanding of historical
events, particularly through individual accounts. Such initiatives can also aid
historical research, as conventional historical narratives often lack
documentation and prioritize the perspectives of decision-makers, thereby
overlooking the experiences of ordinary individuals. BattelAgent illustrates
AI's potential to revitalize the human aspect in crucial social events, thereby
fostering a more nuanced collective understanding and driving the progressive
development of human society.

BattleAgent 是一种结合了大型视觉语言模型和多智能体系统的仿真系统，旨在模拟多个智能体之间以及智能体与环境之间的复杂动态交互，以及领导者的决策过程与普通参与者（如士兵）的观点。该系统通过细粒度的多模态智能体与景观交互展示智能体的当前能力，并提供了可定制的智能体结构以满足特定的情景需求，例如侦察和挖掘战壕等与战争相关的活动。BattleAgent 的技术基础为历史战役建立了详细和沉浸式的环境，使单个智能体能够参与、观察和动态应对战斗场景的演化。这种方法有潜力深化我们对历史事件的理解，特别是通过个体的描述。此类举措还可以帮助历史研究，因为传统的历史叙述经常缺乏文献记载并优先考虑决策者的视角，从而忽视了普通人的经历。BattleAgent 展示了人工智能在重要社会事件中振兴人类因素的潜力，从而促进了更加细致入微的集体理解，并推动了人类社会的进步发展。