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Computerized checkers player can't be beaten

 
 
Miller
 
Reply Sat 21 Jul, 2007 06:22 am
Computerized checkers player can't be beaten

By Robert Mitchum
Chicago Tribune

CHICAGO ?- With uniform pieces and diagonal moves, checkers is simple enough for a child to learn. But to achieve absolute mastery of the game, scientists needed to run hundreds of computers for nearly 20 years, analyzing roughly 500 billion billion scenarios.

By completing the project, a team of Canadian researchers have officially "solved" checkers, creating an unbeatable program that will choose the best move in every possible situation.

This achievement represents a major benchmark in the field of artificial intelligence, which uses games to develop complex problem-solving strategies for computers.

In 1994, a program named Chinook beat the reigning human world checkers champion, a feat that preceded Deep Blue's famous chess defeat of grandmaster Garry Kasparov by three years. But even after proving dominant over humans, finishing the calculations required to solve the game required 13 more years of research.

"Had I known 18 years ago it was this big of a problem, I probably would've done something else," said Jonathan Schaeffer, who led the project at the University of Alberta, "but once I started, I had to finish."

"It's 1 million times bigger than the biggest computation previously solved optimally," he said.

Detailed Thursday on the Web site of the journal Science, methods developed by Schaeffer's team in the process of solving the game may be applicable to other areas, such as business and biology. The resulting program proves that checkers is a "draw" game; in other words, perfect play by both players will always result in a draw.

However, checkers experts say there is no fear that the solving effort will ruin the game for traditional players, amateur or professional.

"No human can possibly memorize the billions of combinations that Dr. Schaeffer has covered," said Richard Beckwith, player representative for the American Checkers Foundation. "You still have to play as you see it, based on your own expertise and knowledge."

The entire solution includes 500,995,484,682,338,672,639 possible board configurations, according to the study, which was funded by the Canadian and Alberta governments.

Murray Campbell, a member of the original Deep Blue team, said the scope of the solution was a testament to the complexity of the game.

"Checkers is actually quite a difficult game ?- much more difficult than most people give it credit for," said Campbell, a research staff member at IBM's T.J. Watson Research Center, in Yorktown Heights, N.Y.

The insights gleaned from teaching computers how to play checkers can be applied to practical, computation-intensive problems. For example, Schaeffer said, the technology could be used to determine the optimal schedule for a massive construction project like the one at Ground Zero in New York.

Schaeffer co-founded a company to use the same approach to hunt for meaningful patterns in long strings of DNA and other biological building blocks.

On Thursday, the Alberta team made the new solving program available to play against on the Internet at www.cs.ualberta.ca/~chinook. Schaeffer, however, doesn't expect it to be a runaway hit.

"In some sense it's not interesting," he said. "People play games for fun, and knowing you can never beat it isn't fun."

Meanwhile, Schaeffer's group has moved on to a more profitable game: designing a poker program capable of beating even professional players. Next week, their current program, named Polaris, will challenge two pros for a $50,000 prize in Vancouver, B.C., at the Association for the Advancement of Artificial Intelligence conference.

Schaeffer speculates that human supremacy at poker remains intact ?- for now.

"I think humans are still better, but it's inevitable that poker will succumb to technology," he said. "Computers will be better than humans eventually, probably in less than five years."
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Type: Discussion • Score: 1 • Views: 692 • Replies: 2
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Miller
 
  1  
Reply Sat 21 Jul, 2007 06:26 am
Could an autistic savant beat the computer at it's own game of checkers?
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Ashers
 
  1  
Reply Wed 25 Jul, 2007 10:33 am
I remember researching this topic a couple of years ago, fascinating really. I looked particularly at Naughts and Crosses with it's 3X3 board for simplicity. Even here, the calculations are much larger than you'd intuitively expect. The point about analysing combinations is the crux of the matter.

I watched a show the other day about one of the few female chess grandmasters and saw her participating in speed chess I think it's called, her manipulation and quick fire calculation of the board and current pieces was astounding. She was beating players left, right and centre with incredible speed. They went on to talk about memory and how she apparently see's chess combinations in her mind (and therefore recognises them) much like we all recognises faces (her brain lights up in the same way). So given this, you can imagine just how fast she can play.

It's completely intuitive, she recognises a chessboard combo and reacts accordingly like we recognises the face of a familiar friend. The savants tap into the kind of incredible computing power and memory capabilities that give computers their edge but if I think given the draw nature of Checkers as noted in the article, the best outcome for a human player will be a draw which would represent a perfect game from both sides. You can imagine the path of a game in contrast with all possible game routes like a tree structure, starting of with the 1st move on a board and trickling down to end games with wins, losses and draws.

Now, when these guys come up with a computer program that can beat human players in a chinese board game called 'Go', that'll be unbelievable. The fascinating thing with Go is that the computations needed are so massive (much bigger than for chess or checkers) that a program using the same techniques as I imagine have been used for this checkers program would take an entire day to make a single move because the computer goes through pretty much all possible combinations whereas human players instinctively dismiss huge lists of possible move combinations based on experience of play and learning which I don't fully understand. What makes Go so difficult for computers and one reason why humans seem to excel at it against computers is that there are in fact so few rules compared to say, Chess. This means the limit to creativity and ingenuity placed on a game of Go is much smaller than for Chess (as far as I can tell) so humans needn't have huge computational powers to the same degree and computers have traditionally struggled to both be creative and to deal with creativity itself. In Go, from the perspective of computers, human think outside the box because the box is too big for computers to deal with.

I think it revolves around brute force search methods that a lot of the chess/checkers programs use involving looking at much larger combinations of plays compared with selective searching which is more akin to human play, using expert knowledge. You get things like machine learning, neural networks and genetic algorithms in artificial intelligence, attempts to mimic the more human process of learning which contrast greatly with brute force methods, I think there is real promise here.
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