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This chapter portrays the historical and mathematical background of dynamic and procedural content generation (PCG). We portray and compare various PCG methods and analyze which mathematical approach is suited for typical applications in game design. In the next step, a structural overview of games applying PCG as well as types of PCG is presented. As abundant PCG content can be overwhelming, we discuss context-aware adaptation as a way to adapt the challenge to individual players’ requirements. Finally, we take a brief look at the future of PCG.
This work demonstrates the potentials of procedural content generation (PCG) for games, focusing on the generation of specific graphic props (reefs) in an explorer game. We briefly portray the state-of-the-art of PCG and compare various methods to create random patterns at runtime. Taking a step towards the game industry, we describe an actual game production and provide a detailed pseudocode implementation showing how Perlin or Simplex noise can be used efficiently. In a comparative study, we investigate two alternative implementations of a decisive game prop: once created traditionally by artists and once generated by procedural algorithms. 41 test subjects played both implementations. The analysis shows that PCG can create a user experience that is significantly more realistic and at the same time perceived as more aesthetically pleasing. In addition, the ever-changing nature of the procedurally generated environments is preferred with high significance, especially by players aged 45 and above.