Some great resources on poker AI: University of Alberta Computer Poker Research Group. Papp 1998 in particular goes into detail about what makes it difficult, briefly: multiple opponents, imperfect knowledge, risk management, agent modeling, deception, and dealing with unreliable information. To these I would add the distinction between optimal and maximal play:
In chess AI, it never really matters what you expect your opponent to do like it does in poker. In chess, you just always try to move the board into the most favorable possible state. A win is a win in chess, but in poker the optimal strategy makes less money than an exploitable, but maximal strategy. But if you’re playing an exploitable strategy, then your opponent can turn around and play a strategy to beat you...much of poker strategy is figuring out how to be one “level” above your opponents for as many hands as possible. And getting an AI to do that is difficult.
Some great resources on poker AI: University of Alberta Computer Poker Research Group. Papp 1998 in particular goes into detail about what makes it difficult, briefly: multiple opponents, imperfect knowledge, risk management, agent modeling, deception, and dealing with unreliable information. To these I would add the distinction between optimal and maximal play:
In chess AI, it never really matters what you expect your opponent to do like it does in poker. In chess, you just always try to move the board into the most favorable possible state. A win is a win in chess, but in poker the optimal strategy makes less money than an exploitable, but maximal strategy. But if you’re playing an exploitable strategy, then your opponent can turn around and play a strategy to beat you...much of poker strategy is figuring out how to be one “level” above your opponents for as many hands as possible. And getting an AI to do that is difficult.