Abstract
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.
Abstract (translated)
本论文介绍了一种名为BattleAgent的模拟系统，结合了大型视觉语言模型和多智能体系统。这个新系统旨在模拟多个代理之间以及代理和环境之间的复杂动态互动。它模拟了领导者的决策过程以及普通参与者的观点，例如士兵。模拟展示了代理的当前能力，其中包括代理和环境之间的精细多模态交互。为了满足特定的情景需求，例如战斗活动，如侦察和挖战壕，该系统开发了可定制的代理结构。这些组件协同工作，以生动且全面的方式重新创建历史事件，同时提供对不同观点个体思维和情感的洞察。BattleAgent的技术基础为历史战斗建立了详细和沉浸式的场景，使个体代理能够参与、观察并动态地响应 evolving battle scenarios。这种方法论有潜力实质性加深我们对历史事件的了解，特别是通过个人的口述。这些举措还可以促进历史研究，因为传统历史叙事通常缺乏资料，并优先考虑决策者的观点，从而忽视了普通个体的经历。BattleAgent展示了AI在关键社会事件中恢复人类方面的潜力，从而推动了更加复杂集体理解和人类社会的持续进步。
URL
https://arxiv.org/abs/2404.15532
