ROLLA, Mo. -- If you were impressed with Deep Blue's 1997 triumph over chessmaster Garry Kasparov, wait until you see the computer that can hold its ownwith the best players of Go, the ancient Asian game that is astronomicallymore complex than chess.
Some of the world's leading Go players claim it will be a century beforesuch a machine exists, but Dr. Donald C. Wunsch is more optimistic. Wunsch,the Mary K. Finley Missouri Distinguished Professor of computer engineeringat the University of Missouri-Rolla, believes the era of truly intelligentmachines -- even computer programs that can match wits with a Go master --is within reach.
The creation of such a machine will involve an "adaptive" computer systemthat is much more intelligent than today's best computers, Wunsch says. Tosucceed at Go, he says, such a system would have to be intelligent enough tolearn the game, adapt to changing conditions, anticipate several possiblemoves, and evaluate the "goban," or playing board -- "to help it learn whichside is winning and which is losing." And it would have to be "smarter" thanDeep Blue, the IBM computer that defeated chess master Garry Kasparov in May1997.
Intelligent systems of the future also must learn to "think" paradoxically-- suspending judgment of contradictory ideas -- in order to better mimichuman intelligence, Wunsch says.
A masterful Go machine "is going to require more human knowledge than chessrequires -- and chess requires a lot of human knowledge," says Wunsch, whois an expert in the field of intelligent computer agents.
"The human experts in Go believe it will be a century before we can make acomputer as good as a Go master," Wunsch says. "I'm more optimistic, but westill have a long way to go."
Go is a strategy game involving two players. Players take turns placingtheir markers, called stones, on the goban, a board with grid lines, withthe objective of controlling the largest part of the goban. The game is aspopular in East Asia as chess is in Russia, Wunsch says. Like chess, Go isbeneficial to the intellectual development of school children, he adds.
Today's best Go computer programs play the game "like an amateur -- likesomeone who has been playing for about six months," says Wunsch, who claimishis own Go skills are "so weak that the stronger computers can beat me."
Go is "easier to learn than chess, but more difficult to master," Wunschexplains. The difficulty in training a computer to play Go is a sheernumbers problem, he adds.
"The game tree in Go has about 10 to the 750th power of possibilities,"Wunsch explains. "Chess, on the other hand, is more like 10 to the 150thpower. In chess, the possible number of games is greater than the number ofatoms in the universe."
So how did Deep Blue become such a chess whiz? According to Wunsch, theanswer lies in Deep Blue's parallel processing power, combined with simple"rule-of-thumb" techniques known as heuristics. Thanks to parallelprocessing -- the use of several processors at once to share the work ofevaluating possible moves -- the computer can evaluate an incredible numberof possible future game positions. By evaluating current board position,king safety and other variables, the heuristics enable Deep Blue toeliminate a large number of moves.
"Computers aren't good at looking at the static position -- to see who'swinning at a given moment -- but they are good at looking at large numbersof possibilities," Wunsch says. "A chess program will look at the board andsay, in effect, 'There are 20 possible moves, but 10 of them are lousy.' Bydoing that, it narrows the possibilities down to a more manageable size."
Still, Deep Blue took a "brute force" approach to processing, Wunsch says.Deep Blue can search through a century of historical chess moves at speedsup to 200 million positions per second. A more truly intelligent approach isneeded if computers are to make the leap Wunsch believes is possible in thecoming decades.
Future intelligent systems will require more emphasis on what Wunsch calls"massively parallel learning systems." Much of the research in intelligentagents -- from Internet search engines and computer spell-checkers tosystems used in high-tech manufacturing -- has focused on developing the"learning systems." Now it's time to emphasize the "massively parallel" partof such systems.
A truly intelligent system is one that can adapt to changes or newinformation and learn from the changes, Wunsch says.
"'Adaptive' and 'intelligent' should be redundant," says Wunsch, whoreceived a National Science Foundation Faculty Early Career Development(CAREER) Award to support his research into intelligent agents. "To call anagent intelligent that is not adaptive is missing the mark." Many Internetsearch engines, for example, are not adaptive -- and therefore not trulyintelligent -- because they cannot change their databases in a variety ofnecessary situations, such as when they encounter a defunct World Wide Website, or when a user ignores the search engine's top hits.
"An adaptive intelligent agent is one that is capable of learning," Wunschsays.
Traditional computer programming, however, limits a computer's ability tothink like a human. Wunsch hopes to see future intelligent systems that movebeyond the raw logic of most systems.
"We need to create logical systems that are able to embrace contradictions,as humans do," Wunsch says. "The computers need to be able to reason on thedata and still be able to prevent coming up with absurdities. We're talkingabout a system that could constrain contradictions."
"Everything we know about computers is still in the stone ages," Wunschsays.
"They're very clumsy, they require experts -- they have all kinds ofproblems," he says. "They should be thought of as prototypes. Intelligentsystems are in their pre-infancy and there's a long way to go."
But that's not to say intelligent systems aren't going to change the way weview computers -- and perhaps sooner rather than later. "Paradoxically, Ithink we're closer to those systems than most people realize," Wunsch says.
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