Introduction. Today, UAVs are becoming an increasingly important tool for performing complex tasks in various fields of application, both civil (economic) and military, as they are particularly effective in dynamically uncertain environments with hard-to-reach areas. In addition, technological advances such as blockchain, artificial intelligence (AI) and machine learning have enabled the development of updated and improved UAV systems. To create and deploy a swarm of UAVs, coordinate actions, manage, and exchange data, a model of a multi-agent system (MAC) based on an ontological representation of knowledge is proposed. This model enables a swarm of UAVs to effectively make decisions in various situations while performing assigned tasks. This approach enables the safety, reliability, and efficiency of the tasks of the UAV group. The purpose of the paper is to develop further the theoretical and practical foundations of the integration of the multi-agent system (MAS) based on the ontological representation of knowledge with the UAV network. This involves the development of a MAS architecture and a hierarchical set of ontologies of different levels. The goal is to create a common data description lan guage, define data semantics to ensure data uniqueness and consistency, provide support for decision-making during UAV swarm management, and swarm survivability in the event of aircraft failures or loss. It is necessary to develop algorithms and a method of dividing a complex task into sub-tasks in a swarm of UAVs among all MAS agents. This is to ensure reliable exchange of messages (data) between agents during the joint performance of the assigned task, and the possibility of dynamic redistribution of roles between UAV agents as needed. Methods. During the research, the general theory of intelligent information technologies was applied; agent theory methods in particular intelligent BDI agents; methods of analyzing the performance of wireless data exchange networks; theory of combinatorial optimization for dividing tasks into subtasks; methods of ontological analysis and descriptive logic to create an ontological hierarchical model of the subject area; methods of enriching ontological models from external semantically marked information resources. Results. As a result of the performed scientific research, the MAS architecture was proposed and its main functions were determined for the decentralized control of a swarm of UAVs. A set of agents with assigned roles was formed, who jointly (cooperatively) perform tasks, exchanging messages, and information with each other, which ensures the survivability of the system (in case of a failure or loss of the device, its task must be distributed among other drones). Plans and scenarios of MAS actions for various situations and means of coordinating actions between agents have been developed to perform the mission by a swarm of UAVs. A hierarchical ontological model of the subject area related to the work of the UAV swarm has been created. The algorithms and methods were based on the integration of semantic technologies that support the MAS during the execution of the UAV swarm mission, decision-making, assessment of the dynamic environment, and response to its changes. Conclusions. An original approach, algorithms, and method for improving the system of decentralized control of a group of UAVs were proposed. Expanding the functionality of the system for maintaining the interaction of a swarm of unmanned systems based on MAS artificial intelligence was suggested. This system was based on ontological models. The models describe knowledge of the subject area, processes of UAV swarm operation, scenarios of actions in difficult situations, distribution of roles to agents, principles of planning, and coordination. The proposed MAS is integrated with the UAV swarm software platform, which makes it possible to improve the efficiency of the decentralized control system and adapt UAVs to dynamic changes in the environment. The practical result of the work will be a prototype of a software agent system that interacts with ontologies while performing simple tasks. The economic significance of the work consists of focusing on the creation of new intelligent information technologies, which were based on AI and knowledge of the subject area, and this significantly increases the efficiency of the functioning of modern systems. Keywords: multi-agent system, ontology, formalization of knowledge, UAV, drone, decentralized control, task allocation.
