Anecdotally: I've never seen a "simple state machine" or "simple behaviour tree" in game AI. We've recently started using deep reinforcement learning for our games and its almost like a miracle how simple, effective and scalable the system is. There are some mentioned problems like designing rewards for player enjoyment, but its definitely got a massive reduction in engineering effort.
> Anecdotally: I've never seen a "simple state machine" or "simple behaviour tree" in game AI.
That's effectively what game AIs are, today. Users want an AI they can model and simulate in their head, and isn't too brutal of a challenge. Today's machine learning cannot provide a model like that.
The whole point of this research is to early study on unknown aspects of game's design and its consequence on user behaviors and game balance. AI should be able to find and exhibit game play unexpected to the designer, which is not easily achievable with a tight control given to game designers. It's more of systematic state space exploration, not making AI fun to play with.
