<![CDATA[Highly complex systems such as smart grids, autonomous transportation networks, and large-scale supply chains present significant challenges for optimization due to high dimensionality, nonlinear interactions, and pervasive uncertainty. Traditional deterministic models often fail under dynamic conditions, while many AI-based approaches lack robustness and stability when confronted with noisy or incomplete data. Addressing these issues, this study proposes a probabilistic decision model designed to enhance AI-driven optimization in uncertain and rapidly changing environments. The model integrates probabilistic graphical structures, Bayesian inference, and AI-based optimization techniques to quantify uncertainty and support adaptive decision-making. Experimental evaluations were conducted using a combination of synthetic datasets, simulation environments, and benchmark scenarios representative of real-world complex systems. Results show that the proposed model achieves significantly higher decision accuracy, improved stability under noisy conditions, and more efficient performance in high-dimensional settings compared with classical optimization, reinforcement learning, and standard probabilistic approaches. The model consistently reduces uncertainty and delivers robust, reliable solutions across a wide range of test conditions.The study presents a scalable, interpretable, and highly effective framework for uncertainty-aware optimization. Its strong performance and generalizability highlight its potential for deployment in critical real-world applications where reliability, safety, and adaptability are essential.]]>
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