The Association for the Advancement of Artificial Intelligence (AAAI) wrapped up its Man vs Machine Poker Challenge yesterday, and, as was expected, the human element won out, although the margin of victory was not overly large.
The results of the game may not be as surprising as some people would like to think. Poker is not an easy game to teach a machine. Computers are inherently deterministic, and the challenge of trying to reduce to mathematical formulaic form a game of "imperfect information" like poker us extremely difficult. . Although a computer can calculate to perfection the odds of receiving a particular hand of cards, which is certainly a big part of the game, it cannot form judgments about whether a certain player is lying or not - which many poker devotees argue is even more important.
However, what the computer can do is to approximate how an opponent will act, collecting information about him all the while, based on his actions when receiving certain cards. This is where most of the AI work on computerized poker games goes: developing algorithms that track opponents' betting habits.
On the other hand, other games such as chess of checkers can easily be transferred to a computerized opponent, since sheer numbers can map out an overwhelming number of possibilities and outcomes. The chessboard is an enclosed space with a only a finite number of possibilities.
Games such as chess or checkers can be replicated well on a computer since such playing style judgments, though occasionally helpful in chess, for example, sheer number crunching can map out an overwhelming number of possible outcomes. The chess board is a closed, determinative place, easily modeled in code.
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