Procedural content generation (PCG) refers to automatically creating game content through algorithms rather than human design. PCG aims to generate a large variety of content from a few parameters. It is not completely random, but uses techniques from AI, math, and other fields to structure the randomness. PCG offers opportunities like high content diversity, faster creation, lower costs, and adaptive gameplay, but also faces challenges like ensuring variety, aesthetics, and multiplayer compatibility. Successful PCG combines domain knowledge, AI techniques, structured randomness, and other specialized algorithms.
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
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
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.
