Cracking Go?
The October issue of IEEE Spectrum has a very interesting article about Go AI players and whether or not one can be created that will beat the best human players. The article is written by Feng-Hsiung Hsu. He was one of the people in the team behind Deep Blue, the chess-playing program that beat Garry Kasparov in 1997, so I guess he knows what he's talking about.
I was a bit disappointed though, after having read the article, since I got the feeling that the solution was more or less the Deep Blue program running on faster hardware and having been enhanced using null-move pruning and caching results of life-and-death analysis. It all sounds a bit... too simple. Life-and-death analysis is tricky business indeed and it doesn't help at all when he basically says that all the leading Go programmers today are too narrow minded in what they do and that their approach(es) won't ever lead to any decent Go AI players.
Until he provides some more details about this I'm going to have to agree with Luke Biewald that he himself may very well have underestimated the problem. It will however be very interesting to see what they come up with in the next few years in the research efforts that Microsoft (where he works) sponsor.
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I had a chat with a friend last year about chess and go games. The search space for GO is just too big to tackle. Even with specialized hardware that prunes, the search space is still too great. The little that I know, which is very little, I think it will a few more decades for major progress.
I can't pretend to be an expert either but my impression is that the problem is making *correct* life-and-death analysis. Since he doesn't even mention that and goes on about better pruning of the game tree I'm afraid that all he'll end up with is a faster, but equally bad, player. I will try to follow what ever comes out of this though. AI Go is just too interesting to ignore ;-)
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