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A Bayesian Network-Based Prediction Method for Continuous Operational Capability During Team Collaborative Tasks
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DOI：10.3390/math13193117.png)
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Abstract
En 中文
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
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