Anatoly Gladun

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.

The publication explores the potential of wiki encyclopedias in addressing the challenge of constructing thesauri for educational courses. It highlights the advantages of utilizing semantic elements from these wiki platforms in the professional development of adult educators.

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.

This article addresses the development and processing of an andragogue profile designed for integration into the adult learning ecosystem. In this context, the andragogue profile is considered as a key element ensuring optimal interaction between the andragogue and the learner. The andragogue facilitates individualized learning conditions by providing a flexible study plan and leveraging the full potential of semantic technologies. The authors emphasize the importance of semantic personalization of the learning process through the creation of personal learning trajectories (PLTs). These trajectories account for diverse learner needs and capabilities, defining individualized interaction plans between the learner and the andragogue. In this research, we propose the method of constructing an extended andragogue profile that incorporates additional characteristics related to various aspects of andragogue`s skills, knowledge and research specialization. These characteristics can be used to develop PLTs for adults on various stages of their lifelong learning (LLL). The extended profile integrates structure elements from both teacher and researcher profiles, supplemented by specific parameters of andragogue`s activities by modeling of characteristics influenced the PLT construction, interaction with learners, and other critical aspects of the educational process. Utilizing this profile can ultimately improve the quality of learning. For a multi- criteria evaluation of the andragogue profile, we apply the Saaty Analytic Hierarchy Process (AHP) method to determine the weighting coefficients of individual parameters such as professional competence, psychological profile, technological literacy and motivational characteristics.

Cloud computing offers advantages such as self-service and dynamic redistribution of resource structures, necessitating the development of effective algorithms for optimal use of hardware infrastructure. Recent studies highlight the problems of resource redistribution in on-site private cloud computing platforms software layers, affecting total energy consumption. Existing approaches to mathematical modeling use combinatorial optimization, game theory, and artificial intelligence but lack an integrated approach to energy consumption optimization. The proposed approach involves creating an original algorithm based on a multi-level architecture developed by the author. This architecture divides objects into seven functional layers, from hardware physical infrastructure to application SaaS layer. The algorithm aims to minimize electricity consumption by isolating critical parameters in the process of redistributing resource structures. The optimization problem is segmented into subproblems, allowing for efficient load placement and energy savings. The model is focused on optimizing the power consumption of on-site private cloud computing platforms using a well-known approach – reducing the number of hardware nodes involved. The idea is to switch off not only individual computing nodes but also cooling nodes when idle. The mathematical formulation of the resource allocation in on-site private cloud computing platforms problem is presented as a Multi-dimensional Bin packing. The algorithm employs dynamic programming to maximize load placement density and reduce the number of hardware nodes. The model provides support technologies for both virtual machines and software containers. The paper presents the original author's algorithm, Optimization of Programmable Infrastructure Resources (OPIR), focused on reducing electricity consumption through combinatorial optimization.

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.

В оглядовій статті проаналізовано андрагогічний аспект проблеми проєктування та реалізації індивідуальних освітніх траєкторій (ІОТ) в умовах цифровізації освіти і суспільства. Здійснено термінологічний аналіз базових понять дослідження. Проаналізовано результати сучасних наукових досліджень в означеній сфері, що дало можливість виявити та увиразнити сутнісні характеристики ІОТ, їх функціональні можливості. Зазначено, що проєктування та впровадження ІОТ є важливим складником професіоналізації освіти дорослих. Обґрунтовано, що індивідуальні освітні траєкторії дорослих проєктуються та реалізуються з урахуванням мотивації, потреб, особистих життєвих обставин, досвіду, стилю навчання тощо. Акцентовано увагу на тому, що ІОТ, зокрема, вирізняються персоніфікованістю, динамічністю й гнучкістю, адаптивністю, спрямованістю на сприяння досягненню цілей професійного, особистісного зростання. Виокремлено рівні проєктування та реалізації ІОТ дорослих: цінннісно-мотиваційний, контекстний, змістовий, технологічний, інформаційно-ресурсний, оцінно-рефлексивний. Конкретизовано дії здобувачів освіти та андрагогів у межах виокремлених рівнів проєктування та реалізації ІОТ. Виявлено, що андрагогічні тренди в освіті відображають концептуальні, змістово-процесуальні актуалітети впровадження концепції освіти впродовж життя й зумовлюються технологічними інноваціями, соціально-демографічними зрушеннями різних рівнів, потребами ринку праці, попитом на персоніфіковане навчання тощо. Акцентовано увагу на таких трендах в освіті, як: інтеграція цифрових технологій в освітній процес; упровадження дієвих інструментів для оцінювання результатів навчання, забезпечення зворотного зв’язку; забезпечення доступності, адаптивності та інклюзивності навчання; персоналізація індивідуальних освітніх траєкторій на засадах андрагогіки і хьютагогіки; зростання попиту на корпоративне навчання працівників; андрагогічний супровід навчання здобувачів тощо. Зазначено, що цифрові рішення (штучний інтелект, VR/AR, адаптивні платформи тощо) як ключові інструменти забезпечення ефективності андрагогічного підходу сприяють персоналізації, доступності, неперервності освіти дорослих. Ключові слова: освіта дорослих; індивідуальні освітні траєкторії; андрагогічні тренди в освіті; цифровізація.