Procedurally Generated Content in Games and its Effect on Player Experience

Abstract

This thesis investigates the relationship between algorithmic complexity, procedural content complexity, and user experience for procedural content generation (PCG) in video games. It examines whether increased algorithmic sophistication leads to more varied and higher-quality content, and how such content compares to handcrafted levels in terms of player experience.

A prototype game was developed in Unity using C# to compare two PCG approaches, a basic random walk algorithm and a more advanced graph rewriting system. The generated game levels were evaluated using both player-centric and technical methods. Player experience was assessed through Think-Aloud play-testing, semi-structured interviews, the Game Experience Questionnaire (GEQ), and gameplay data analysis. Algorithmic complexity was determined using Halstead complexity measurements and Cyclomatic Complexity.

Results indicate that while the graph rewriting algorithm produced more diverse and engaging level structures than the random walk approach, it also introduced greater development complexity and reduced maintainability. Additionally, gameplay issues arose in procedurally generated content that were not present in handcrafted levels. These findings highlight the trade-offs between PCG complexity, content control, and content variety.

Ultimately, the thesis emphasises the need for controlled iteration, player-focused evaluation, and careful variable isolation in PCG system development. It provides actionable insights for game developers on when and how to apply procedural generation effectively while balancing content quality, development time, and system maintainability.