I gave a talk on procgen last year where one of my slides attempted to sum things up thusly: > In my experience good procgen is the creative application of bor

I gave a talk on procgen last year where one of my slides attempted to sum things up thusly: > In my experience good procgen is the creative application of boring algorithms. Your key weapons are imagination and iteration at the design stage - but the actual code tends to be simple, and fancier algos don't make your content any better. > In contrast with ML, you live or die by how magical your data and algorithms are, but you have little direct control over the output so it's not a very creative process. You tend to pull the levers and hope that what comes out will be suitable. To me that difference is why ML tends not to work well for procgen problems. With creative tasks we tend to have top-down design constraints that we want to impose (e.g. a minecraft village needs to fulfill various requirements for the game engine to treat it as a village), and the task is to make content "interesting" while meeting those constraints. With procgen this is straightforward - you start with the constraints, and creatively iterate on the "interesting". Whereas with ML you often get interesting for free, but if your output doesn't fulfill this or that constraint, it's not very obvious how to proceed.