Uncertainty-Aware Decision-Making of Robust Bayesian Networks with Distributed Data Security in Adaptive Artificial Intelligence Systems

INTRODUCTION: The reliability of decision-making in adaptive artificial intelligence (AI) systems is limited by uncertain factors such as noise in multi-source sensing data and conflicts in decision-making objectives. Furthermore, in distributed multi-node collaborative environments, challenges such as cross-node data leakage and edge-node privacy risks further exacerbate decision uncertainty. OBJECTIVES: To address this issue, an uncertainty-aware decision-making method for adaptive AI systems based on robust Bayesian networks is proposed, with a specific focus on distributed data security and privacy protection. METHODS: A robust Bayesian network-based model is proposed. Multi-source sensing data are fused using adaptive weighted minimum mean square error optimization. Secure distributed aggregation is achieved through encrypted aggregation and differential privacy mechanisms. Federated transfer learning is introduced to prevent raw data sharing, and minimax estimation is used to correct missing-data bias. RESULTS: Experimental results on distributed inspection robot clusters show that the proposed method effectively achieves uncertainty-aware decision-making under dynamic and obstacle-affected scenarios while maintaining privacy protection and data security. CONCLUSION: The proposed framework improves decision robustness and privacy preservation in distributed adaptive AI systems by integrating Bayesian inference, federated learning, and secure data fusion strategies.