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Computer poker program sets its own Texas Hold'em
strategy |
A Carnegie Mellon University computer scientist has
demonstrated that you don't necessarily need to know
much about poker to create a computer program that can
play a winning hand of Texas Hold'Em. A knowledge of
game theory, not the specialized expertise of a human
poker player, is at the heart of the poker robot called
GS1 developed by Tuomas Sandholm, director of Carnegie
Mellon's Agent-Mediated Electronic Marketplaces Lab, and
graduate student Andrew Gilpin.
Though not yet the equal of the best human players, GS1
outperformed the two leading "pokerbots" in playing
heads-up, limit Texas Hold'Em in tests at Carnegie
Mellon earlier this year. Both of GS1's opponents were
commercially available programs that, like other
pokerbots, incorporate the expertise of human poker
players. GS1, by contrast, develops its strategy after
performing an automated analysis of poker rules.
Sandholm and Gilpin have since developed an improved
version of their game-theory-based program, called GS2,
which will compete in the American
Association for Artificial Intelligence's first Computer
Poker Competition during the 21st National Conference on
Artificial Intelligence July 1620 in Boston.
Much as computer chess was an early test of artificial
intelligence (AI), computer poker has emerged as an even
greater AI challenge. "Poker is a very complex game,"
said Sandholm, a professor of computer science in
Carnegie Mellon's School of Computer Science. "Computer
poker programs really require sophisticated technology."
Unlike chess, where the status of all of the chess
pieces is known to both players, poker forces players to
make decisions based on incomplete information. "You
don't know what the other guy is holding," Sandholm
explained. And the sheer number of possible combinations
of cards dealt, cards on the table and bets in
two-player Texas Hold'Em games -- 1018, or a billion
times a billion -- makes it impossible for even the
fastest computers to fully analyze every hand.
This element of uncertainty and the vagaries of luck
inherent in randomly dealt cards actually make poker a
better test of AI's prowess than chess. "A lot of
real-world situations have uncertainty in them and you
have to deal with the uncertainty," Sandholm said. An
algorithm (sequence of steps) that can capably play
poker might also be useful in electronic commerce
applications, such as sequential negotiation and
auctions, he said.
Electronic commerce is a major research focus for
Sandholm. He has developed the fastest algorithms for
matching supply and demand, which can now be expressed
in significantly more detail than before. He is the
founder, chairman and chief scientist of CombineNet, a
company that helps Fortune 1000 organizations save money
and time on procurement. More than $20 billion has been
sourced through CombineNet's system, generating in
excess of $2.5 billion in savings for customers.
Using AI techniques to automatically set rules for
electronic commerce is another direction Sandholm has
pioneered. These programs generate mechanisms that can
govern electronic auctions, elections or negotiations.
In his computer poker research, Sandholm has developed
pokerbots that precompute the strategies for playing the
first two rounds of Texas Hold'Em, the so-called
"pre-flop" and "flop" rounds, when players are first
dealt two cards and then three additional cards are
positioned face-up. For the third and fourth betting
rounds, the "turn" and the "river," his pokerbots update
the probability of each possible hand by taking into
account betting as well as the revealed cards. The
strategy for those rounds is then computed in real-time
for the setting at hand.
To reduce the computational complexity, GS1 and GS2
automatically recognize strategically equivalent hands.
For instance, 25,989,600 distinct hands are possible in
the second round, but only about a million are
strategically different. That's still too many to
compute, so the pokerbots group strategically similar
hands together. The end result is 2,465 groups, a small
enough number to allow computational analysis. |
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