It’s been a while since I looked into this, but when I did, the big problem was behavioral modeling.
All the math in poker is local—the state of the game in one hand influences the state of the game in the next only insofar as it affects the amount of chips players have on hand. That makes it easy to build a poker AI that’ll wipe the floor with innumerate players. But in also narrows the scope enough that it’s possible for more sophisticated human players to mentally solve for their probability of winning a given hand, or at least approximate it pretty well, and many do. An AI can’t do much better than that with pure statistics, the only way to squeeze more comparative advantage out is for it to become better at gauging playstyle and hiding information about its own patterns of play than human players are. And that’s a much harder problem than building a tree of possible moves in chess.
It’s been a while since I looked into this, but when I did, the big problem was behavioral modeling.
All the math in poker is local—the state of the game in one hand influences the state of the game in the next only insofar as it affects the amount of chips players have on hand. That makes it easy to build a poker AI that’ll wipe the floor with innumerate players. But in also narrows the scope enough that it’s possible for more sophisticated human players to mentally solve for their probability of winning a given hand, or at least approximate it pretty well, and many do. An AI can’t do much better than that with pure statistics, the only way to squeeze more comparative advantage out is for it to become better at gauging playstyle and hiding information about its own patterns of play than human players are. And that’s a much harder problem than building a tree of possible moves in chess.