ALGORITHMS AND METHODS OF PROCEDURAL GENERATION AND ADAPTIVE DIFFICULTY IN PLATFORMER GAMES

Abstract

The article focuses on the study of algorithms and methods for procedural generation of game content and adaptive difficulty in 2D platformers. The relevance of approaches combining modular architecture with dynamic difficulty adjustment based on player actions is substantiated, aiming to ensure variability, replay value, and maintain the dynamism of game content. The paper proposes a layered model of level design, where basic environmental elements (ground, platforms, obstacles) are generated sequentially, while higher layers (enemies, traps, decorations) are governed by consistent placement rules. A key role in creating natural geometry and controlled randomness is played by Perlin noise and its fractal extensions (octaves): combining multiple frequencies and amplitudes allows large-scale contours to merge with fine terrain details. The proposed adaptive difficulty system is integrated into the generator by modifying noise parameters (scale, persistence, octaves) and enemy behavioral attributes (speed, attack frequency, quantity), determined by the player’s performance index. The practical implementation was carried out in the Unity engine using the C# programming language and the Tilemap approach; code examples for heightmap generation and adaptive controller integration are provided. The developed algorithms and methods are engine-agnostic and can be easily integrated into C++ environments, particularly Unreal Engine and Cocos2d-x, demonstrating their cross-platform applicability and scalability. It is shown that the combined approach maintains a balance between unpredictability and control, reduces the cost of manual level design, and enhances user engagement through personalized gameplay experiences. The obtained results can be applied in educational prototypes, indie projects, and serve as a foundation for further research involving machine learning methods in generative game design.