But there are middle grounds between machine learning and simple state-machine approaches. Probabilistic state machines, online learning, (especially reinforcement learning), sound to me like they would be ideal in a game environment.
You want the character to act realistically according to his position in the world relative to the player? Define his observations, define his "reward", define a set of possible actions and state transitions, and let him go! I'm curious whether this approach is starting to be more used in game AI, it seems like a natural fit to me. Maybe some pre-training is needed so that it doesn't act randomly at first.
However, the idea of handicapping the AI by actually reducing his observations or his set of possible actions seems like a much more natural approach than more explicit handicapping methods.
You want the character to act realistically according to his position in the world relative to the player? Define his observations, define his "reward", define a set of possible actions and state transitions, and let him go! I'm curious whether this approach is starting to be more used in game AI, it seems like a natural fit to me. Maybe some pre-training is needed so that it doesn't act randomly at first.
However, the idea of handicapping the AI by actually reducing his observations or his set of possible actions seems like a much more natural approach than more explicit handicapping methods.