Changes

For many years Chess (and perhaps more recently Go) has served as the Drosophila of AI research. Decades of research culminated in the defeat of Garry Kasparov by DEEP BLUE in May 1997. There is still an active research community that uses Chess as a test-bed for AI research (as seen in this journal), but the game is limited in the types of challenges that it can offer to the AI researcher. Being a game of perfect information (both players know the full state of the game at any given point) with a relatively small branching factor, researchers have reduced the challenge of building a strong AI for Chess to merely one of deep brute-force search. The research challenges are to create a good evaluation function, and to design an effective search algorithm. This “solution” to Chess is unappealing to many AI purists. Nevertheless, alternative AI approaches have been largely ineffective. Poker, as an experimental test-bed for exploring AI, is a much richer domain than Chess (and Go). # Imperfect information. Parts of the game state (opponent hands) are not known. # Multiple players. Many popular poker variants can be played with up to 10 players. # Stochastic. The dealing of the cards adds a random element to the game. # Deception. Predictable play can be exploited by an opponent. Hence, deceptive play is an essential ingredient of strong play (e.g., bluffing). # Opponent modelling. Observing your opponent(s) and adjusting your play to exploit (perceived) opponent tendencies is necessary to maximize poker winnings. # Information sparsity. Many poker hands end in the players not revealing their cards. This limits the amount of data available to learn from.

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