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Mysterious Tartrate Conquers All At Go 65

Rubyflame writes "As noted on the Sensei's Library resource for the ancient Chinese boardgame Go, Tartrate, a very strong and mysterious Go player, has recently returned to the Kiseido Go Server (KGS) after a long absence. The game records can be found here. Tartrate first appeared in March, and has yet to be defeated - his identity is unknown." This intriguing story is a little reminiscent of Bobby Fischer's online chess appearances - the Go players on KGS even log their Tartrate number: "tartrate has a tartrate number of 0. If you have played a game with tartrate, your tartrate number is 1. If you have played a game with someone whose number is 1, your number is 2, and so on."
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Mysterious Tartrate Conquers All At Go

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  • AI? (Score:5, Interesting)

    by torpor ( 458 ) <ibisum@g[ ]l.com ['mai' in gap]> on Tuesday January 06, 2004 @09:44AM (#7890315) Homepage Journal

    How feasible is it that its an AI being used to play tartrates games, anyone know?

    I've seen some amazing Go games in my life (while I lived in Tokyo) and I know that the Go mojo is not something you're going to just up and code without being really, really good yourself ... but it is interesting that since we know nothing much about tartrate himself, the first thing that came to my mind is 'someone is running a good Go bot' ...

    Not to detract from his skills, mind. I'm just interested if any of those who have played him could not have been defeated by some of the various Go-playing algorithms which are floating around out there. Some of them are too good.
  • Re:AI? (Score:3, Interesting)

    by Infinite93 ( 664963 ) on Tuesday January 06, 2004 @10:08AM (#7890504)
    I would be very suprised if it was an AI. Last time I looked (I will admit it has been while), the best AI out there was comparable to an average amateur player. There was even a $1M prize until Y2000 (donor died) for any AI that could beat a Taiwanese professional player. Of course, it could be a very clever way of performing a kind of 'turing test' for someone's next generation AI.
  • by cthulhubob ( 161144 ) on Tuesday January 06, 2004 @10:15AM (#7890543) Homepage
    Before Tartrate got so famous (infamous?) amateur go players were keeping track of how many games they were away from having played Honinbo Shuusaku of the Edo period.

    My Shuusaku number is 5 -- Shusaku (0) - Iwasaki Kenzo (1) - Honinbo Shusai (2) - Iwamoto Kaoru (3) - James Kerwin (4) - Ethan Baldridge (5).

    One of the coolest games on the KGS archives is Tartrate vs. Redrose (Irina Shikshina, a Russian woman who is a 1st dan Korean professional). Tartrate was black and played his first move on tengen (the center of the board), which is an unusual opening. There were two ENORMOUS ko fights, and everybody thought Redrose had won after the first one was over. Check it out, it's a great game.

    If anybody wants a Shuusaku number of 6 and/or a Tartrate number of 3, my username is ethanb on both KGS and DGS (kgs.kiseido.com, and www.dragongoserver.net).
  • by cthulhubob ( 161144 ) on Tuesday January 06, 2004 @12:58PM (#7892372) Homepage

    You should attend a workshop taught by Yang Yilun. He's a 7-dan Chinese pro who teaches in the U.S. Usually the workshops run all weekend for about $200-250. He is an excellent teacher and has written several books, including one coming out soon from Slate and Shell [slateandshell.com].

    The most impressive thing I've ever seen is at the one workshop I've been to by him. He took all of the students (eighteen), divided us up into pairs (so they can discuss moves with one another), and played us all simultaneously. Then after beating us all (even the pair composed of Keith Arnold, 5 dan and Eagles Song, 4 dan) we cleared off the boards, then he sat down with the first pair, replayed their game from memory, and commented on what they could have done better. Then he replayed the second game from memory... and kept going all the way around the circle.

    He's got another workshop coming up in June, I believe. It's in New Jersey. I'm definitely making the trek.

  • A good book about Go (Score:4, Interesting)

    by Laplace ( 143876 ) on Tuesday January 06, 2004 @03:12PM (#7893826)
    Read "The Master of Go" by Yasunari Kawabata. It's an amazing novel about not only the culture and play of Go, but also the rise of Western ideals after the fall of Imperial Japan. It won the Nobel Prize for Literature. Read it; you'll like it.
  • Re:AI? (Score:3, Interesting)

    by cgenman ( 325138 ) on Tuesday January 06, 2004 @09:06PM (#7898372) Homepage
    Right, but a sufficiently large search space will not be reached in a human time frame. You are talking about the number of open spaces left on a board with 361 possibilities for the first move, 129,960 possibilities for the second move, 46 million possibilities for the third move, etc. By move ten you have 33 septillion possibilities, and you still have no easy way to gauge whether or not the move was a good one. By the time each player has made 15 moves, or a solid start to a game, your search space is 20 google, and that's assuming no taking has taken place. Yes, you can do things with rotation and identical board positions, but 5 Google is still a lot for a 486. If you want the whole possibilities, trimmed down to only the best moves a computer can make at any given point, that's still a space of... little TI-92 programming here... 4*10^384 possible trees.

    A Neural Net of Go would suffer from a similar problem of scale... You have 19x19 locations, with 3 possibilities at each. That's a learning space of 17 sexquinquagintillion, or 17 octovigintilliard for our british friends. We could again divide by 4 for each possible rotation, but 4*10^171 is still pretty big. Assuming each board discovered and knew the one "best" next move to make, the storage required would be enormous. If there are 10^81 atoms in the universe (a high estimate), and one were to further ludicrously assume that there are as many universes as there are atoms in this one, each QUARK in every atom would have to store a bit of data to have a pre-stored list of the best next moves. That's not including how much space would be required to store all of the associated failure rates with the other positions, let alone a system capable of reading it all. Learning computers require a lot more RAM than a straight programmed one. And unlike image recognition, blurry go boards just won't cut it.

    Genetic programs and neural nets are great at some things, but certain problems don't play out so easily. Any program that will play well at Go will have to have some extremely high-level thinking, of which we are not capable of producing or breeding today. Otherwise, the sample space just falls apart.

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