Introduction into Procedural Content Generation by Yogie Aditya
- 1. Introduction into Procedural Content Generation Yogie Aditya Niji Games
- 2. Procedural Content Generation Extra Credits
- 3. Procedural Content Generation • Procedural content generation (PCG) refers to creating game content automatically, through algorithmic means. -Togelius,Yannakakis, Stanley, Browne • PCG should ensure that from a few parameters, a large number of possible types of content can be generated. - Doull • Procedural Content Generation is the process of using techniques based on AI, maths and other disciplines to automatically create game content. - University of Strathclyde
- 4. Is PCG totally Random? Extra Credits
- 8. Opportunities of PCG • High diversity of the resulting assets • Faster than any human designer could ever be • Significantly reduces production costs • Allows for a mixed-initiative approach to level design • Content automatically implemented in the engine • Can save vital system resources • Players can influence the parameters of the game world • Possibility of automatically analyzing player behavior
- 9. Challenges of PCG Satisfying a high number of constraints (e.g. full connectivity) • Finding these constraints and tweaking unintuitive parameters of the system can degenerate into trial and error Produce aesthetically pleasing results • Levels can become too similar to each other Maximize the expressive range (variety of results) • Can decrease co-op multiplayer playability May require spending too much time on inventing a sophisticated level generator
- 10. Extra Credits
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- 14. The Ingredients? • Domain knowledge • Artificial intelligence • Structured randomness • Multi-layering • Filters, limits & restrictions • Specialized algorithms • Gameplay integration
- 15. The Ingredients? (Con’t) Domain Knowledge • To generate something you need to know it • PCG typically aims at building an artificial level designer, usually needs domain knowledge about level design Artificial Intelligence • Need algorithms that can work on complex knowledge and generate plausible content • Search-based methods, L-systems, evolutionary computation, fractals, cellular automata, agent-based methods, planning, graphic programming, etc.
- 16. PCG Implementation Example • Koch Snowflakes • Grid Based • Chunk-based approach • Noise
- 17. Koch Snowflakes (N = 0)
- 18. Koch Snowflakes (N = 1)
- 19. Koch Snowflakes (N = 2)
- 20. Koch Snowflakes (N = 3)
- 21. Koch Snowflakes (N = 4)
- 22. Grid Based
- 23. Grid Based
- 24. Grid Based
- 25. Grid Based
- 31. Noise
- 32. Noise
- 33. Noise
- 34. That’s all folks! Thank you Any Question?