Katerina Khala

The paper proposes a dynamic ontological model for the semantic-predictive self-organization of an unmanned aerial vehicle (UAV) swarm. A multi-layer ontological model of the UAV swarm is developed, comprising a static core, a dynamic instance layer, an agent layer with local knowledge projections, and a mechanism for semantic synchronization of agents to ensure knowledge consistency in a distributed multi-agent system. A mathematical model is formulated to formalize the processes of knowledge alignment, scenario forecasting, and optimal action selection. The organization of interactions between structural layers is examined, providing knowledge exchange, adaptive role allocation, and support for collective agent behavior. Experimental simulation conducted in the Gazebo environment for forest fire monitoring by a group of 12 UAVs confirms the effectiveness of the proposed approach: semantic consistency reached 91%, the accuracy of critical event prediction increased by 17–20%, and network load decreased by 83.4%. The developed model ensures scalability, robustness to the loss of individual agents, and efficient execution of cooperative tasks, making it suitable for the development of intelligent control systems for autonomous UAV swarms.

Mission planning for unmanned aerial vehicle (UAV) swarms in multi-agent systems (MAS) necessitates efficient task allocation to ensure survivability, self-organization, and successful mission completion. This paper presents a hybrid planning approach combining role-based task allocation (RBTA) and an ontology- driven methodology to formalize MAS domain knowledge. This integration reduces computational overhead, optimizes flight control execution, and enhances system autonomy. Mathematical models for RBTA are developed, incorporating key cost factors (time, energy, agent suitability) and task prioritization mechanisms, along with dynamic role reassignment strategies to address UAV failures. The proposed algorithm is formalized in a graph-based scheme comprising five core modules: role assignment, task allocation, swarm self-organization, monitoring and adaptation, and performance evaluation. Ontologies ensure semantic consistency among agents, while RBTA facilitates planning through predefined roles (leader or scout). Empirical results obtained using Python demonstrate a 15–20% reduction in mission execution time compared to conventional methods, alongside a 25% decrease in communication overhead. The proposed approach proves particularly effective in dynamic environments where rapid adaptation and fault tolerance are critical.

The chapter considers the application of ontology models enriched with external resources for intelligent planning of a UAV swarm mission. The relevance of ontologies for formalizing knowledge about missions, environment, and threats is substantiated. A method for enriching ontologies from online knowledge bases, encyclopedias, dictionaries, and other open sources using the information field concept for preliminary information assessment and processing is proposed. A methodology for adaptive ontology updating is presented. The proposed approach using online external information resources allows to take into account the dynamics of updating current information about research objects, reduces ontology update time, provides high-quality knowledge for UAV swarm self-organization, movement coordination, threat detection, and flexible response to the external environment.

The monograph is devoted to a comprehensive analysis of the role and possibilities of applying artificial intelligence in scientific research. It examines the theoretical, methodological, and applied aspects of using data analysis methods, machine learning and deep learning, as well as intelligent decision support systems in the natural, technical, and humanities sciences. Special attention is given to the comparative analysis of artificial intelligence models, the development of ontological approaches, and the application of intelligent technologies in the fields of defense, complex systems management, chemistry, materials science, energy, biotechnology, and the study of social processes. Particular emphasis is also placed on issues of academic integrity and the use of artificial intelligence in the preparation of scholarly works. The monograph is intended for researchers, educators, doctoral students, and all professionals interested in the application of artificial intelligence in scientific research.

Introduction. Ontological analysis is a significant area in the field of intelligent information technologies, particularly in the development of legal systems where there is a continuous need for efficient management and exchange of legal knowledge. Due to the complexity of legal systems, the application of semantic technologies allows for formalizing legal concepts, simplifying the development of ontological models for representing legal knowledge, and integrating heterogeneous legal information systems. Additionally, incorporating fuzzy logic is essential for handling uncertainty and incompleteness in legal information. The purpose of the paper is to develop a legal ontology model capable of efficiently processing ambiguous legal terms and concepts while automating the classification and analysis of legal documents. The primary objective is to create a flexible system for formalizing legal knowledge that accounts for the specifics of legal acts, enhances the law enforcement process, and supports informed decision-making. Methods. The study employs semantic ontological modeling methods to create legal ontologies and fuzzy logic methods for processing vague and incomplete data. Modern tools and ontology development languages, such as Protege and OWL (Web Ontology Language), are used alongside machine learning techniques for classifying and analyzing legal texts. The approach also explores integrating fuzzy logic elements for evaluating document similarity and representing complex legal concepts. Results. A legal ontology model was developed to automate the classification and analysis of legal terms, concepts, and their relationships. The proposed methodology enables the system to extract information from various legal sources and analyze legal documents while addressing ambiguous data. Testing demonstrated improved classification accuracy and increased efficiency in retrieving legal norms from large volumes of unstructured data. Conclusions. The proposed legal ontology model, incorporating elements of fuzzy logic, significantly enhances the representation and processing of legal knowledge. The methodology includes grammar analysis and the construction of document ontological models, allowing for more precise comparisons of document similarities and differences. The semantic approach proved more effective than the k-means clustering method for key phrase classification. Integrating fuzzy sets into the ontology model facilitates the description of imprecise information and supports reasoning with varying levels of completeness. Ongoing work aims to expand the Ukrainian-language version of the legal ontology to address practical challenges in knowledge-based legal systems. The obtained results serve as a foundation for further advancements in intelligent information systems within the legal domain.

One of the main challenges today is deploying a coordinated group of unmanned aerial vehicles (UAVs), especially those based on multi-agent systems (MAS), which offer the advantages of decentralized management. Key benefits of using MAS in UAV systems include enhanced joint problem-solving capabilities, improved system survivability, and increased availability and scalability when executing complex missions. Effective data exchange between drones requires interoperable information, ensuring unambiguous communication and a formalized understanding of each drone's role and function. Furthermore, such systems need to establish protocols for cooperation, role reassignment in cases of failure or loss, and reliable identification of other UAVs. Implementing an ontological model can help address these challenges by formalizing knowledge about system policies for various complex scenarios, thereby enhancing the MAS's ability to accomplish its objectives efficiently.

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.

Implementation of applied artificial intelligence systems requires novel approaches to the formalization and modeling of domain knowledge. Collecting data about information objects, which represent components of intelligent systems, calls for creation of reliable information field of the object with predetermined restrictions. These restrictions affect the structure of the field and ensure its filling from the open Web environment. The paper proposes means and methods of pre-processing the data field (classification, clustering, associative relationships) and integration of accumulated resources. Also, we suggest a method of step-by-step development of the structure of the information field, which begins with the definition of the information object of research around which the field will be built. The problem of the reliability of information resources from which the information field is filled is solved by analytical methods and the method of semantic analysis of metadata. It's provided interpretation, recognition, and selection of reliable data relevant to the issue being solved. The issue of accuracy of data recognition and interpretation of accumulated data is solved on the basis of methods for calculating semantic proximity and semantic distance between semantic models. The practical implementation of the obtained results created a multi-agent e-commerce system based on the ontological approach.

It is becoming clear with growing complication of cybersecurity threats, that one of the most important resources to combat cyberattacks is the processing of large amounts of data in the cyber environment. In order to process a huge amount of data and to make decisions, there is a need to automate the tasks of searching, selecting and interpreting Big Data to solve operational information security problems. Big data analytics is complemented by semantic technology, can improve cybersecurity, and allows you to process and interpret large amounts of information in the cyber environment. Using of semantic modeling methods in Big Data analytics is necessary for the selection and combination of heterogeneous Big Data sources, recognition of the patterns of network attacks and other cyber threats, which must occur quickly to implement countermeasures. Therefore to analyze Big Data metadata, the authors propose pre-processing of metadata at the semantic level. As analysis tools, it is proposed to create a thesaurus of the problem based on the domain ontology, which should provide a terminological basis for the integration of ontologies of different levels. To build a thesaurus of the problem, it is proposed to use the standards of open information resources, dictionaries, encyclopedias. The development of an ontology hierarchy formalizes the relationships between data elements that will be used in future for machine learning and artificial intelligence algorithms to adapt to changes in the environment, which in turn will increase the efficiency of big data analytics for the cybersecurity domain.