@inproceedings{lin-etal-2024-battleagent,
title = "{B}attle{A}gent: Multi-modal Dynamic Emulation on Historical Battles to Complement Historical Analysis",
author = "Lin, Shuhang and
Hua, Wenyue and
Li, Lingyao and
Chang, Che-Jui and
Fan, Lizhou and
Ji, Jianchao and
Hua, Hang and
Jin, Mingyu and
Luo, Jiebo and
Zhang, Yongfeng",
editor = "Hernandez Farias, Delia Irazu and
Hope, Tom and
Li, Manling",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-demo.18",
pages = "172--181",
abstract = "This paper presents \textbf{BattleAgent}, a detailed emulation demonstration system that combines the Large Vision-Language Model (VLM) and Multi-Agent System (MAS). This novel system aims to emulate complex dynamic interactions among multiple agents, as well as between agents and their environments, over a period of time. 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. This methodology holds the potential to substantially improve visualization of historical events and deepen our understanding of historical events especially from the perspective of decision making. The data and code for this project are accessible at \url{https://github.com/agiresearch/battleagent} and the demo is accessible at \url{https://drive.google.com/file/d/1I5B3KWiYCSSP1uMiPGNmXlTmild-MzRJ/view?usp=sharing}.",
}
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<abstract>This paper presents BattleAgent, a detailed emulation demonstration system that combines the Large Vision-Language Model (VLM) and Multi-Agent System (MAS). This novel system aims to emulate complex dynamic interactions among multiple agents, as well as between agents and their environments, over a period of time. 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. This methodology holds the potential to substantially improve visualization of historical events and deepen our understanding of historical events especially from the perspective of decision making. The data and code for this project are accessible at https://github.com/agiresearch/battleagent and the demo is accessible at https://drive.google.com/file/d/1I5B3KWiYCSSP1uMiPGNmXlTmild-MzRJ/view?usp=sharing.</abstract>
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%0 Conference Proceedings
%T BattleAgent: Multi-modal Dynamic Emulation on Historical Battles to Complement Historical Analysis
%A Lin, Shuhang
%A Hua, Wenyue
%A Li, Lingyao
%A Chang, Che-Jui
%A Fan, Lizhou
%A Ji, Jianchao
%A Hua, Hang
%A Jin, Mingyu
%A Luo, Jiebo
%A Zhang, Yongfeng
%Y Hernandez Farias, Delia Irazu
%Y Hope, Tom
%Y Li, Manling
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F lin-etal-2024-battleagent
%X This paper presents BattleAgent, a detailed emulation demonstration system that combines the Large Vision-Language Model (VLM) and Multi-Agent System (MAS). This novel system aims to emulate complex dynamic interactions among multiple agents, as well as between agents and their environments, over a period of time. 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. This methodology holds the potential to substantially improve visualization of historical events and deepen our understanding of historical events especially from the perspective of decision making. The data and code for this project are accessible at https://github.com/agiresearch/battleagent and the demo is accessible at https://drive.google.com/file/d/1I5B3KWiYCSSP1uMiPGNmXlTmild-MzRJ/view?usp=sharing.
%U https://aclanthology.org/2024.emnlp-demo.18
%P 172-181
Markdown (Informal)
[BattleAgent: Multi-modal Dynamic Emulation on Historical Battles to Complement Historical Analysis](https://aclanthology.org/2024.emnlp-demo.18) (Lin et al., EMNLP 2024)
ACL
- Shuhang Lin, Wenyue Hua, Lingyao Li, Che-Jui Chang, Lizhou Fan, Jianchao Ji, Hang Hua, Mingyu Jin, Jiebo Luo, and Yongfeng Zhang. 2024. BattleAgent: Multi-modal Dynamic Emulation on Historical Battles to Complement Historical Analysis. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 172–181, Miami, Florida, USA. Association for Computational Linguistics.