SCOTT SIMON, HOST:
Ken Regan is a kind of chess detective. He's a computer scientist and an international chess master, who played with the likes of Bobby Fischer as a kid. Which gives him particular skills to help recognize cheating in chess, which, he says, is becoming more common. Ken Regan has created a new algorithm to help detect test cheating. He's profiled this month in "U.S. Chess" magazine and joins us now from Buffalo. Thanks for much for being with us.
KEN REGAN: Thank you.
SIMON: You know, I must say, when your work was first explained to me, there was a part of me that wondered if chess cheating was just a matter of somebody going, you know, hey, Katy Perry's over there, and upsetting the board.
REGAN: Yes, yes. Well, not quite. People don't distract and then take a rook off the board. That doesn't happen.
SIMON: So how does somebody cheat in chess?
REGAN: The most common way is having the game on your smart phone or handheld device and going into the bathroom surreptitiously to check it.
SIMON: So people are consulting their smart phones, because there are algorithms that will tell them what the propitious next move is?
REGAN: Yes. There are chess engines that are very strong - stronger than any human player, apparently even running on the reduced hardware of smart phone.
SIMON: I mean, shouldn't this be pretty easy to detect? I can't imagine that you need to be Sherlock Holmes to see if somebody is consulting their iPhone while they're playing a chess game?
REGAN: Well, that's been true. In some cases, people have had behavior that's suspected, and they've been followed and caught. But in the case in Dortmund, Germany, last August, it was very subtle. You had to notice that the person had his left hand in his pocket and was doing a little fingering with the fingers. If you're not watching closely, you might not see this.
SIMON: Is part of the rise in chess cheating due to the fact that - if I can put it this way - computers are becoming more accomplished at chess, too?
REGAN: Yes. Absolutely. I mean, you know, 17 years ago, Gary Kasparov fell to a supercomputer. But now the saying is that your iPad today is as good as a supercomputer was 20 years ago. So put to do the two together, and that kind of power, as well as improved chess algorithms, is at everyone's fingertips.
SIMON: Explain the kind of algorithm that you're talking about. Help guide us through it.
REGAN: Different chess positions have different character. Some have just a single move for you to stay in the game or a single move to keep your advantage. When that's the case, a strong chess player is highly likely to find such a move, as will the computer, so you'll get a match. So when there's a case where a person says, wow, this guy made a lot of moves that are just like the computer, I analyze the game and say, well, was it a forcing game? Were most of these moves ones that most people would find? And my model will spit out a number that says, yes, and, therefore, this is not a great deviation. In other cases, it'll say, whoa, wait a second. This is a much higher correspondence than these positions will would allow.
SIMON: Professor Regan, how do you analyze these games? How do you keep trap of track of them?
REGAN: Well, one of the great things about chess is the entire record of play is part of the public record. So I can feed the moves into a computer, and the computer will analyze them.
SIMON: Well, what are the odds of somebody being falsely accused?
REGAN: I deal with accusations, whispers, public statements, grouses that people make. And, usually, my model shows, no, this play really was within expectation. The other side is, yes, it's a great danger that the statistics might falsely accuse someone. As a failsafe, I have taken data - many millions of pages of data from the entire history of chess, including all the performances by Bobby Fischer and Gary Kasparov. So I have an idea of the distribution of what happens by nature.
SIMON: Ken Regan, a chest detective and computer science professor at the State University of New York at Buffalo. Thanks much for being with us.
REGAN: Thank you very much. Transcript provided by NPR, Copyright NPR.