Differential Models for Optimizing the Strategy of Intelligent Agents in a Dynamie Environment of Uncertainty

This paper presents differential models for optimizing the behavior and strategies of intelligent agents operating in dynamic environments with uncertainty. The research focuses on the mathematical representation of state dynamics, the construction of optimization mechanisms, and the development of adaptive decision-making strategies under incomplete or stochastic information. The proposed approach is based on the integration of differential equations, control theory, and stochastic optimization methods. The results demonstrate that differential models provide a formal framework for analyzing stability, adaptability, and efficiency of intelligent agents, allowing for the identification of optimal strategies in complex uncertain systems. This work contributes to the development of theoretical foundations for designing intelligent systems with high robustness and efficiency in uncertain dynamic environments