Research on situation awareness agent based on large models

Autor: SUN Yifeng, LIAO Shufan, WU Jiang, LI Fulin
Jazyk: čínština
Rok vydání: 2024
Předmět:
Zdroj: Zhihui kongzhi yu fangzhen, Vol 46, Iss 2, Pp 1-7 (2024)
Druh dokumentu: article
ISSN: 1673-3819
DOI: 10.3969/j.issn.1673-3819.2024.02.001
Popis: Aimming at the multitudinous battlefield situation information and the difficulty in recognizing the changing trends, based on large models, a situation awareness agent and an intelligent situation awareness inductive method are proposed. Starting from cognitive concepts and combining the abstractness and embodiment characteristics of agents, three key components in the construction of agents have been clarified: learning environment, memory mode, and knowledge generation mechanism. The architecture of the battlefield situation awareness agent is designed, including memory component, planning component, execution component, evaluation component, and key points for agent training. In the long-term memory component, based on the modeling characteristics of complex battlefield states, the paper discusses the application of large language models, multimodal large models and large sequence models.
Databáze: Directory of Open Access Journals