Information Technologies for Creating Intelligent Control Systems in Distributed Computer and Communication Environments
DOI:
https://doi.org/10.15407/intechsys.2026.03.062Keywords:
artificial intelligence, intelligent control systems, distributed computer and communication environments, distributed intelligence, machine learning, reinforcement learning, multi-agent systems, autonomous systems, decision support, network-centric controlAbstract
Introduction. The increasing complexity of distributed computer and communication environments requires the development of advanced intelligent control technologies capable of operating under conditions of uncertainty, dynamic network topology, communication delays, and large-scale data processing. Traditional control approaches are often unable to provide the required level of adaptability and autonomy in such environments, which has led to the widespread adoption of artificial intelligence technologies.
The purpose of the paper is to analyze existing information technologies used for the development of intelligent control systems in distributed computer and communication environments and to identify promising directions for their further evolution.
Results. The paper reviews contemporary artificial intelligence technologies, including machine learning, deep learning, reinforcement learning, expert systems, fuzzy logic, intelligent agents, and multi-agent systems. Particular attention is paid to the issues of autonomous decision-making, distributed intelligence, adaptive control, and coordination of autonomous agents operating in network-centric environments. The advantages, limitations, and application domains of the considered technologies are analyzed and compared.
The study demonstrates that multi-agent architectures, reinforcement learning algorithms, and distributed artificial intelligence technologies provide the highest potential for the implementation of intelligent control functions in complex distributed environments. Furthermore, emerging approaches based on digital twins, edge computing, federated learning, and semantic communications significantly expand the capabilities of next-generation intelligent control systems.
Conclusions. The obtained results can serve as a theoretical foundation for the design and implementation of adaptive intelligent control systems capable of autonomous operation and real-time decision-making in distributed computer and communication infrastructures.
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