Forget chess – go for Go
03.08.2002 Programming games like chess has been a "relative snap" and has
"succumbed to the power of the processor", says this New York Times
article. "Go is different. To date, no computer has been able to achieve a
skill level beyond that of the casual player." You will need to register
(free of charge) to read the original
piece. We have also prepared a summary of the NYT article with some points
of discussion. You can join the debate here...
August 1, 2002
In an Ancient Game, Computing's Future
By KATIE HAFNER
Editorial commentary by the ChessBase staff is
given in red italics.
Early in the film "A Beautiful Mind," the mathematician John Nash
is seen sitting in a Princeton courtyard, hunched over a playing board covered
with small black and white pieces that look like pebbles. He was playing Go, an
ancient Asian game. Frustration at losing that game inspired the real Mr. Nash
to pursue the mathematics of game theory, research for which he eventually won a
Nobel Prize.
In recent years, computer experts, particularly those specializing in
artificial intelligence, have felt the same fascination — and frustration.
Programming other board games has been a relative snap. Even chess has
succumbed to the power of the processor. Five years ago, a chess-playing
computer called Deep Blue not only beat but thoroughly humbled Garry Kasparov,
the world champion at the time. That is because chess, while highly complex, can
be reduced to a matter of brute force computation.
Kasparov was not "thoroughly humbled" by
the machine: He won the first match in 1996 and lost the second in 1997,
narrowly while dominating the machine in most games. The overall score in the
twelve games between the two is +4, =5, –3, which adds up to 5.5-4.5 for
Kasparov.
Go is different. Deceptively easy to learn, either for a computer or a human,
it is a game of such depth and complexity that it can take years for a person to
become a strong player. To date, no computer has been able to achieve a skill
level beyond that of the casual player.
Programmers working on Go see it as more accurate than chess in reflecting
the ineffable ways in which the human mind works. The challenge of programming a
computer to mimic that process goes to the core of artificial intelligence,
which involves the study of learning and decision-making, strategic thinking,
knowledge representation, pattern recognition and, perhaps most intriguingly,
intuition.
Today's computers do not try to mimic the
processes of the human mind. They always do "brute force
computation". At the same time the may display behavior which is
practically indistinguishable from human activity that is driven by
"learning, decision-making, strategic thinking, knowledge representation,
pattern recognition and intuition". Prime examples are Fritz, Junior,
Shredder, Hiarcs, Tiger and other members of that crowd.
"A good Go player could make a move and other players say, `Yes, that's
a good move,' but they can't explain to you why it's a good move, or how they
even know it's a good move," said Dr. John McCarthy, a professor emeritus
at Stanford University and a pioneer in artificial intelligence.
Dr. Danny Hillis, a computer designer and chairman of the technology company
Applied Minds, said that the depth of Go made it ripe for the kind of scientific
progress that comes from studying one example in great detail. "We want the
equivalent of a fruit fly to study," Dr. Hillis said. "Chess was the
fruit fly for studying logic. Go may be the fruit fly for studying
intuition."
It is possible that programming Go will lead to
breakthroughs in simulating human pattern recognition and intuition. We
believe that it is far more likely that a number-crunching brute force
solution will be found first. Chess went through exactly the same process.
David Fotland, [a programmer and chip designer in San Jose, Calif., who
created and sells The Many Faces of Go, one of the few commercial Go programs,]
said, "writing a strong Go program will teach us more about making
computers think like people than writing a strong chess program."
With over a hundred teams programming chess during
the last fifty years one would have expected results in this area than in the
relative new endeavor of programming a computer to play Go. Although chess
certainly requires learning, decision-making, strategic thinking, knowledge
representation, pattern recognition and intuition when it is played by human
beings, none of this turned out to be practically feasible in a computer
program. It would be quite remarkable if the breakthrough comes in an area
which is much less well understood than chess.
Dr. Reiss, [a computer scientist in London] who works on Go full time, said
"I think in the long run the only way to write a strong Go program is to
have it learn from its own mistakes, which is classic A.I., and no one knows how
to do that yet," Mr. Fotland said. A few programs have some learning
capabilities built into them.
Dr. Reiss said he had come up with an idea for a new Go program that would
learn by analyzing professional games. But to pursue his idea would require too
much work, he said, depriving him of time to continue making updates to his
current program.
We have seen exactly this happen in numerous
computer chess projects. The developers have explored the possibility of human
decision making, pattern recognition and intuition, only to come up with a
traditional brute force program in the end, one which only crunches numbers
but at the same time displays behavior that in humans requires pattern
recognition and intuition.
It seems unlikely that a computer will be programmed to drub a strong human
player any time soon, Dr. Reiss said. "But it's possible to make an
interesting amount of progress, and the problem stays interesting," he
said. "I imagine it will be a juicy problem that people talk about for many
decades to come."
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