frob said:
That's something that would be a poor fit for machine learning. There isn't much to reinforce or a way to get better, it is a binary state, either they made the shape or they didn't. During training a character could perform millions of pickup and placements and never create a single ‘symbol of power’.
I meant to use ML only to detect the symbol a human player has made, so pattern recognition just like reading human written text.
frob said:
But for game mechanics, where human designers need to fine tune values, humans adjust probabilities and frequencies and other weights, those tend to be a terrible fit for ML. Stick with state machines, behavior trees, and human adjustable math formulas.
It would be interesting to know if a chess ML-AI can learn to play worse than optimal, and so adjust difficulty to a range of human players to loose on purpose, but pretty tight so causing satisfaction. Probably that's no harder problem than to learn chess at all.
We will see what the future brings. New games or new genres might deal with new limitations, problems and options in ways we can not foresee yet. At least i hope things keep changing so the medium remains interesting.