BattleAgent: Multi-modal Dynamic Emulation on Historical Battles to Complement Historical Analysis
Shuhang Lin, Wenyue Hua, Lingyao Li, Che-Jui Chang, Lizhou Fan,, Jianchao Ji, Hang Hua, Mingyu Jin, Jiebo Luo, Yongfeng Zhang

TL;DR
BattleAgent is a multi-modal emulation system that simulates historical battles with detailed agent interactions, aiming to enhance understanding of individual experiences and decision-making in historical events.
Contribution
It introduces a novel multi-modal, multi-agent emulation framework that captures complex interactions and diverse perspectives in historical battle scenarios.
Findings
Demonstrates detailed, dynamic battle simulations with multi-modal interactions.
Provides insights into individual perspectives and decision-making processes.
Enables customizable agent behaviors for specific historical activities.
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
Taxonomy
TopicsDigital Humanities and Scholarship
