> Currently if I understand correctly, the traditional approach is to define a bunch of actions and figure out a realistic way to choose what action to perform,

> Currently if I understand correctly, the traditional approach is to define a bunch of actions and figure out a realistic way to choose what action to perform, tuning a state machine carefully to make sure the character is realistic as well as beatable. I can imagine this takes a lot of work. The aim is not to produce a realistic response, but fun. It's much, much easier to produce a response that isn't fun than to produce one that is, and this is the problem. Often a complex, systemic AI needs much more tuning to be fun than a simpler, more authored AI. Fun is an emotional response so it's hard to define systemically. > In other words, the designer can now treat the character states as a black-box model, and instead worry directly about its input-output behavioural relationship, something I think an artist might have better intuition for compared to dealing with explicit state machines. Game designers are not the same as graphic designers or artists, they work exclusively in logic. Flow charts, decision trees and giant spreadsheets are standard tools of the trade. However, there's certainly scope for being able to take the game designer's logic to a higher, more abstract level. Being able to remove some of the micro-management would be useful. I'm not sure that machine learning helps there though, it seems more of a planning problem.