That's often a problem with what's sometimes called "constructive" PCG, i.e. manually writing an algorithm that generates variations of content. It's somewhat h

That's often a problem with what's sometimes called "constructive" PCG, i.e. manually writing an algorithm that generates variations of content. It's somewhat hard to craft an algorithm that produces a ton of variations that don't look trivially like variations, at least without a lot of careful tweaking. > I'd be really interested in learning about any work in the "interestingness" of procedural content. Some of the AI research on PCG goes in that direction. Instead of writing a constructive algorithm, you instead try to specify what content you want in some formal way, and then use a solver to generate content meeting the description (a genetic algorithm, constraint solver, etc.). I.e. start from the goal rather than from the algorithm. It is still difficult to quantify "interesting content" though! Instead it usually needs to be something more specific, like "a level with the following properties: A, B, C". As a shameless plug, two colleagues and I are working on a book that surveys the current research: http://www.pcgbook.com http://www.pcgbook.com