ANALYSIS OF APPROACHES TO PROCEDURALLY GENERATED GAME LEVELS IN WEB-ORIENTED SYSTEMS

Abstract

The current task of analyzing approaches to the intellectual analysis of procedurally generated game levels in web systems is considered. Modern methods of generating procedural content allow for the automated creation of a large number of game maps. However, ensuring their playability, balance, and compliance with game design requirements remains a pressing task. An approach is proposed that combines algorithms for generating spatial structures with tools for automated route analysis and level quality assessment. Special emphasis is placed on the use of pathfinding algorithms, creating heat maps of movement, and modeling dynamic changes in the environment. The possibility of using breadth-first search (BFS) algorithms to determine the shortest routes, as well as spatial randomization methods to determine alternative movement paths, is considered. It is studied that the combination of procedural generation and analysis mechanisms improves the quality of generated levels and reduces the time required for manual testing. The proposed approach can serve as a decision-making tool for computer game developers, artificial intelligence researchers, and developers of interactive systems. 
The practical significance of this work lies in establishing methodological foundations for automatic assessment of structural features of game environments. The research results demonstrate the potential of integrating graph analysis algorithms, procedural content generation, and web technologies for developing adaptive digital environments of the next generation. In addition, the possibilities of using client-side web technologies for implementing interactive analysis systems without server infrastructure were investigated. This reduces computational effort and ensures high speed of interaction with the user. The presented approach contributes to the automation of digital environment design processes and creates prerequisites for the implementation of intelligent mechanisms for adapting content to user behavior. 
The results are applicable not only to the video game industry, but also to such industries as modeling of complex spatial systems, robotics, educational simulators, and virtual environments. A promising approach for future research is the integration of machine learning methods for automatic adjustment of generation parameters, prediction of user behavior, and design of personalized digital spaces taking into account individual interaction scenarios.