With the growing popularity of game services that require constant content updates to retain players, automating the generation of adaptive playable characters has become an urgent task. This article examines existing approaches to character generation, including evolutionary algorithms, and in-session adaptation systems. Current solutions are limited by their inability to provide sufficient long-term adaptation to individual player styles and their reliance on manual design.

To address these limitations, we propose a three-component system that integrates: player action modeling based on gameplay replays using reinforcement learning (RL) agents, character generation through combinatorial mechanics and parameter balancing, automatic validation via simulations to assess balance and alignment with a player’s individual style.

This work synthesizes contemporary research, highlighting the potential of generative methods to reduce development costs for game services. The results could accelerate prototyping and enhance the long-term viability of live-service projects.

The paper presents a comparative analysis of methods for representing three-dimensional objects to perform real-time Boolean operations in the Unity game engine environment. Four main approaches are considered: polygonal representation based on constructive solid geometry (CSG), sign distance functions (SDF), voxel methods and CAD-systems with boundary representation (B-Rep) and NURBS-surfaces.

An experimental study of the performance of polygonal algorithms of Boolean operations and SDF functions based on ray marching implementation is carried out. It is revealed that polygonal methods are characterized by high initial system construction costs, but provide stable performance during long operations and preservation of transformation results. SDF functions demonstrate high speed of operations and flexibility in creating smooth transitions between objects, but are limited in application for long-term tasks due to the peculiarities of the computational model.

The areas of effective application of each approach are identified: polygonal methods are recommended for tasks requiring precise geometric control and integration with traditional graphics pipelines, while SDF functions are optimal for procedural generation, multilayer material rendering and creation of dynamic visual effects. The results of the study can be used in the development of interactive simulators, game applications and virtual reality systems.

The developed algorithm of the visual perception system for game agents, implemented in the Unity game engine, is presented. The proposed method is based on the comparison of images from two cameras, taking into account complex visual effects (lighting, shadows, camouflage), and supplemented with line-of-sight verification, taking into account the speed of the object, and the mechanics of gradual detection. Testing of the system has shown a significant increase in realistic detection compared to traditional methods, while maintaining performance within a small additional load on the processor. The algorithm was optimized using Unity Job System and dynamic camera activation. The scientific literature on similar solutions has also been analyzed and their strengths and weaknesses have been identified. The results can be applied in video game development to create realistic behavior of non-player characters, especially in games with stealth elements.

This article presents the design and development of the educational video game, conceived as a tool for the promotion and preservation of traditional Cuban music of the Son genre among children and adolescents. The proposal combines cultural content with contemporary game mechanics, generating an immersive and didactic experience. Its pedagogical foundations, playful structure, and potential impact on the formation of cultural awareness from an early age are described.

The article is devoted to the study of the gaming experience of users and the formation of a cognitive model of their interaction with the new video game "Koshchejskie prodelki", which is at an early stage of development. The UX research methodology proposed by the authors, as well as the results obtained during it, demonstrate how qualitative data on the perception, emotions, feelings, behavior patterns and achievements of players help optimize the process of creating video games, making them not only entertaining, but also psychologically effective. The results of the work have practical value for indie developers who, with limited resources, strive to create high-quality gaming products.

Human Pose Estimation (HPE) has become one of the most relevant topics in computer vision research. This technology can be applied in various fields such as video surveillance, medical care, and sports motion analysis. Due to the increasing demand for HPE, many libraries for this technology have been developed in the last 20 years. Since 2017, many HPE algorithms based on skeletal model have been published and packaged into libraries for easy use by researchers.

These libraries are important for researchers who want to integrate them into real-world applications for video surveillance, medical care, and sports motion analysis.

This paper investigates the strengths and weaknesses of four popular HPE advanced human pose recognition libraries that can run on mobile devices: Lightweight OpenPose, PoseNet, MoveNet, and Blase Pose.

This paper presents a neuro-fuzzy algorithm for high-speed grayscale image segmentation based on a modified defuzzification method using triangular membership functions. The aim of the study is to analyze the effect of simplifying the defuzzification formula on the accuracy and contrast of object selection. The proposed approach includes adaptive learning of the weight coefficient, which allows dynamically adjusting the defuzzification process depending on the target values. The paper compares the basic method of averaging membership values and a modified version taking into account nonlinear weights. Experiments conducted on 1024x720 images demonstrate that the developed algorithm provides high segmentation accuracy and improved object contrast with minimal computational costs. The results confirm the superiority of the proposed method over traditional approaches, emphasizing the prospects for applying artificial intelligence in computer vision problems.

The modern information space contains a lot of data, but they are often poorly structured, difficult to find and not always correct. This creates additional difficulties during researches, so digital spaces of scientific knowledge are currently being formed, in particular, based on knowledge graphs.

To ensure the quality of information, such graphs are often filled with data manually, which is time-consuming. Therefore, the creation of a tool that provides the ability to automate process of filling a graph with data, as well as ensures data quality, will simplify and speed up the process of forming digital spaces of scientific knowledge.

Methods for automating the filling of the graph with data are proposed, including parallel control of their integrity. Based on the proposed methods, a software module has been developed, the mechanisms of its functioning and its architecture are described.

The main methods of numerical simulation (finite difference method, finite element method, Monte Carlo method, Runge–Kutta method) are presented. The main parameters used to optimize numerical modeling algorithms in terms of code execution time and efficient use of processor resources are considered. The main disadvantages of multithreading related to data synchronization, deadlocks and race conditions and methods for eliminating them based on the use of mutexes and atomic operations using the Monte Carlo method as an example were analyzed.

Metadata of scientific publications are used to build catalogs, determine the citation of publications, and perform other tasks. Automation of metadata extraction from PDF files provides means to speed up the execution of the designated tasks, while the possibility of further use of the obtained data depends on the quality of extraction. Existing software solutions were analyzed, after which three of them were selected: GROBID, CERMINE, ScientificPdfParser. A procedure for comparing software solutions for recognizing texts of scientific publications by the quality of metadata extraction is proposed. Based on the procedure, an experiment was conducted to extract 4 types of metadata (title, abstract, publication date, author names). To compare software solutions, a dataset of 112,457 publications divided into 23 subject areas formed on the basis of Semantic Scholar data was used. An example of choosing an effective software solution for metadata extraction under the conditions of specified priorities for subject areas and types of metadata using a weighted sum is given. It was determined that for the given example CERMINE shows efficiency 10.5% higher than GROBID and 9.6% higher than ScientificPdfParser.

Signature methods are a powerful tool for time series analysis, transforming them into a form suitable for machine learning tasks. The article examines the fundamental concepts of path signatures, their properties, and geometric interpretation, as well as computational methods for various types of time series. Examples of signature method applications in different fields, including finance, medicine, and education, are presented, highlighting their advantages over traditional approaches. Special attention is given to synthetic data generation based on signatures, which is particularly relevant when working with limited datasets. The experimental results on generating and predicting student digital learning trajectories demonstrate the effectiveness of signature methods for machine learning applications in time series analysis and forecasting.

This study focuses on the application of machine learning methods to improve the quality of test items. The research includes a review of the subject area and the implementation of two enhancement methods: similar question retrieval and distractor quality assessment. The first method involves testing five transformer-based models for generating text embeddings and six clustering algorithms. The second method uses the same transformer models in combination with three classification algorithms. Experimental results demonstrated the high effectiveness of the proposed approaches in solving both tasks.