The Research on AOS Cognitive Modeling and Construction Method of Command Decision-Making Behavior Model

In the context of modern joint operations, the battlefield is characterized by rapidly growing information, complex situations, diverse missions, and shortened decision-making time windows, which pose significant challenges to traditional data models. This study proposes a cognitive modeling framewo...

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Bibliographic Details
Published inInternational Conference on Automation, Control and Robotics Engineering (Online) pp. 254 - 258
Main Authors Shen, Yue, Huang, Wenfei, Zhu, Qing, Zuo, Yi, Huang, Chenyu, Ye, Ling, Zheng, Shaoqiu, Wang, Gangliang, Jin, Hao, Guo, Qinglang
Format Conference Proceeding
LanguageEnglish
Published IEEE 16.07.2025
Subjects
AOS Framework
Artificial intelligence
Cognition
Cognitive Modeling
Command Decision-Making
Computational modeling
Data models
Decision making
Intelligent Combat Simulation
Knowledge Graph
Knowledge graphs
Military computing
Robots
Stability analysis
Windows
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ISSN2997-6278
DOI10.1109/CACRE66141.2025.11119568

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Summary:In the context of modern joint operations, the battlefield is characterized by rapidly growing information, complex situations, diverse missions, and shortened decision-making time windows, which pose significant challenges to traditional data models. This study proposes a cognitive modeling framework based on the AOS (Agent-Objective-Scene) paradigm, leveraging the high-speed computation and powerful storage capabilities of big data and artificial intelligence. The framework integrates military knowledge graphs and adopts cognitive decision-making behavior modeling methods to construct a cognitive decision-making system for complex operational environments. Experimental results demonstrate that this method significantly enhances the system's decision support capabilities in battlefield simulations. This research not only contributes innovative outcomes in the domain of complex battlefield command and decision-making but also provides new ideas and practical approaches for cognitive modeling in artificial intelligence.
ISSN:2997-6278
DOI:10.1109/CACRE66141.2025.11119568