To provide a bit of context, I'm a software engineer & game enthusiast (card games, especially). The thing is I've always been interested in AI oriented to games. In college, I programmed my own Gomoku AI, so I'm a bit familiar with the basic concepts of AI applied to games and have read books & articles about Game Theory as well.
My issue comes when I try to analyze AI's for Imperfect Information games like (Poker, Magic the gathering, Hearthstone, etc). In most cases, when I found an AI for Hearthstone, it was either some sort of Monte Carlo or MinMax strategy. I honestly think, although it might even provide some decent results, it will still be always quite flat and linear, since it doesn't take into account what deck the opponent is playing and almost always tries to follow the same game-plan, since it will not change based on tells your opponent might give away via cards played (a hint that a human would catch).
I would like to know if using Neural Networks would be better than just using a raw evaluation of board state + hands + Hp each turn without taking into account learning about possible threads the opponent might have, how to deny the opponent the best plays he could make, etc.
My intuition tells me that this is way harder and far more complex.
Is that the only reason the NN method is not used? Has there been any research to prove how much efficiency edge would be between those 2 approaches?