Class Procedural Content Generations

Content generation is increasingly important in contemporary game development. Although there is a huge range of tools that facilitate the development of digital artifacts, there is still a high cost of labor and time for its realization. Due to this cost, algorithms for content generation are increasingly attractive, as is the case of the SpeedTree system, for example, widely used in the industry. As such, this curricular unit will focus specifically on learning and implementing several algorithms for procedural content generation with strong presence in industry and academia, although always keeping an eye on new developments in the area.

Not applicable

PC1. PCG foundations: randomness, distributions, noise (Perlin, Simplex, blue noise, octaves, domain warping). PC2. Mathematical fields: fractals, SDFs, Boolean ops, shader procedural textures (2D/volumetric). PC3. Simulation PCG: cellular automata, agents, emergence vs control PC4. Terrain: heightmaps, stitching, erosion, chunked worlds, streaming, volumetric terrain, Marching Cubes. PC5. Grammars: formal grammars, L-systems, implicit rules, loot systems. PC6. Graphs: mission/progression graphs, structure vs representation. PC7. Constraints: hard/soft, propagation, rewrite systems, state machines. PC8. Levels: assembly, modular kits, dungeons, navigation-aware layouts. PC9. Evaluation: playability, heuristics, difficulty. PC10. Evolution: genetic representations, fitness, mutation, crossover, co-evolution, limits. PC11. Learning PCG: statistical methods, WFC-style systems, neural overview. PC12. Mixed-initiative: tools, player modeling, adaptive PCG, ethics.

The course follows a lecture-based model combined with guided independent work, integrating innovative methods through the systematic analysis of real video game industry cases. Each topic is linked to concrete game examples, connecting theory, algorithms, and PCG techniques to professional solutions. The approach strengthens the link between technical aspects and design and production decisions, promoting contextualized learning, critical thinking, and knowledge transfer to new problems.

Shaker, N., Togelius, J., & Nelson, M. J. (2016). Procedural content generation in games. Springer. Millington, I. (2019). AI for games (3rd ed.). CRC Press.