TY - GEN
T1 - BattleAgent
T2 - 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024
AU - Lin, Shuhang
AU - Hua, Wenyue
AU - Li, Lingyao
AU - Chang, Che Jui
AU - Fan, Lizhou
AU - Ji, Jianchao
AU - Hua, Hang
AU - Jin, Mingyu
AU - Luo, Jiebo
AU - Zhang, Yongfeng
N1 - Publisher Copyright:
© 2024 Association for Computational Linguistics.
PY - 2024
Y1 - 2024
N2 - 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. The demo is accessible at https://drive.google.com/file/d/1I5B3KWiYCSSP1uMiPGNmXlTmild-MzRJ/view?usp=sharing.
AB - 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. The demo is accessible at https://drive.google.com/file/d/1I5B3KWiYCSSP1uMiPGNmXlTmild-MzRJ/view?usp=sharing.
UR - http://www.scopus.com/inward/record.url?scp=85215713428&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85215713428&partnerID=8YFLogxK
U2 - 10.18653/v1/2024.emnlp-demo.18
DO - 10.18653/v1/2024.emnlp-demo.18
M3 - Conference contribution
AN - SCOPUS:85215713428
T3 - EMNLP 2024 - 2024 Conference on Empirical Methods in Natural Language Processing, Proceedings of System Demonstrations
SP - 172
EP - 181
BT - EMNLP 2024 - 2024 Conference on Empirical Methods in Natural Language Processing, Proceedings of System Demonstrations
A2 - Farias, Delia Irazu Hernandez
A2 - Hope, Tom
A2 - Li, Manling
PB - Association for Computational Linguistics (ACL)
Y2 - 12 November 2024 through 16 November 2024
ER -