Artificial intelligence

Abstract: This paper presents an integrated approach to real-time processing and analysis of large-scale data streams using artificial intelligence methods, multi-agent systems, and multi-layered control architectures. Particular attention is devoted to combining technologies that are traditionally applied separately: unmanned aerial vehicle (UAV) control systems, situational centers, streaming video services, and distributed sensor networks. The proposed model accounts for the stochastic nature of the environment, the dynamic behaviour of data flows, and resource constraints, which are critical factors for real-time systems. The study develops a multi-level multi-agent architecture that includes central, regional, and edge nodes, as well as local agents such as drones, sensors, and client-side video devices. A mathematical formalization of agent interaction and reward functions is proposed to ensure a balance between service quality, latency, packet loss, and energy consumption. The use of multi-agent reinforcement learning (MARL) algorithms is introduced to support adaptive decision-making in real time, along with the development of a digital twin of the environment for predicting future system states using deep and generative models. The results demonstrate that the integration of edge computing, hierarchical decision-making, and local agent autonomy significantly enhances system resilience, provides stable performance under external disturbances, and enables effective infrastructure scaling. The findings form a scientific foundation for designing adaptive, robust, and self-learning next-generation digital ecosystems capable of intelligent processing of large data streams in real time.

Keywords: artificial intelligence, multi-agent systems, real-time data processing, unmanned aerial vehicles, edge computing, reinforcement learning, video streaming, quality of experience (QoE), system resilience.

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