A Bayesian Network-Based Prediction Method for Continuous Operational Capability During Team Collaborative Tasks

Shanran Wang, Liping Pang, Pei Li (), Tingting Jiao, Xiyue Wang and Hao Yin
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Shanran Wang: School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China
Liping Pang: School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China
Pei Li: College of Aeronautical Engineering, Civil Aviation University of China, Tianjin 300300, China
Tingting Jiao: China North Vehicle Research Institute, Beijing 100072, China
Xiyue Wang: School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China
Hao Yin: China North Vehicle Research Institute, Beijing 100072, China

Mathematics, 2025, vol. 13, issue 19, 1-22

Abstract: In the context of team collaborative tasks, continuous operational capability represents a crucial indicator of operational efficiency and a pivotal area of current research. A reduction in the continuous operational capability of team members will inevitably result in an increase in the error rate, which will prevent the completion of the task. In certain circumstances, this may even result in more severe consequences. To guarantee that team members possess optimal operational capabilities, it is imperative to conduct research on continuous operational capability prediction. This paper presents a Bayesian network-based continuous operational capability prediction model for team collaborative tasks. The model is developed based on the causal relationship of the continuous operational capability evolution, and through the improvement on the Bayesian network so that it can be suitable for individual personnel. The experimental verification demonstrates that the model produces accurate results and can be employed to predict the continuous operational capability and its changing trend.

Keywords: Bayesian network; team collaborative task; continuous operational capability; prediction model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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