[josh_carterPDX]

Unlike chess or Go, where both players see the entire board, poker involves hidden information, your opponents’ hole cards. This makes it an incomplete-information game, which is far more complex mathematically. The AI must reason not only about what could happen, but also what might be hidden.

Even in 2-player No-Limit Hold’em, the number of possible game states is astronomically large — on the order of 10³¹ decision points. Because players can bet any amount (not just fixed options), this branching factor explodes far beyond games like chess.

Good poker requires bluffing and balancing ranges and deliberately playing suboptimally in the short term to stay unpredictable. This means an AI must learn probabilistic, non-deterministic strategies, not fixed rules. Plus, no facial cues or tells.

Humans adapt mid-game. If an AI never adjusts, a strong player could exploit it. If it does adapt, it risks being counter-exploited. Balancing this adaptivity is very difficult in uncertain environments.