Chris Maddison also produced very good (in fact much better) results using a deep convolutional network during his internship at Google. Currently waiting for publication approval, I will post the paper once it is passed.
Aja On Mon, Dec 15, 2014 at 2:59 PM, Erik van der Werf <erikvanderw...@gmail.com > wrote: > > Thanks for posting this Hiroshi! > > Nice to see this neural network revival. It is mostly old ideas, and it is > not really surprising to me, but with modern compute power everyone can now > see that it works really well. BTW for some related work (not cited), > people might be interested to read up on the 90s work of Stoutamire, > Enderton, Schraudolph and Enzenberger. > > Comparing results to old publications is a bit tricky. For example, the > things I did in 2001/2002 are reported to achieve around 25% prediction > accuracy, which at the time seemed good but is now considered unimpressive. > However, in hindsight, an important reason for that number was time > pressure and lack of compute power, which is not really related to anything > fundamental. Nowadays using nearly the same training mechanism, but with > more data and more capacity to learn (i.e., a bigger network), I also get > pro-results around 40%. In case you're interested, this paper > http://arxiv.org/pdf/1108.4220.pdf by Thomas Wolf has a figure with more > recent results (the latest version of Steenvreter is still a little bit > better though). > > Another problem with comparing results is the difficulty to obtain > independent test data. I don't think that was done optimally in this case. > The problem is that, especially for amateur games, there are a lot of > people memorizing and repeating the popular sequences. Also, if you're not > careful, it is quite easy to get duplicate games in you dataset (I've had > cases where one game was annotated in chinese, and the other (duplicate) in > English, or where the board was simply rotated). My solution around this > was to always test on games from the most recent pro-tournaments, for which > I was certain they could not yet be in the training database. However, even > that may not be perfect, because also pro's play popular joseki, which > means there will at least be lots of duplicate opening positions. > > I'm not surprised these systems now work very well as stand alone players > against weak opponents. Some years ago David and Thore's move predictors > managed to beat me once in a 9-stones handicap game, which indicates that > also their system was already stronger than GNU Go. Further, the version of > Steenvreter in my Android app at its lowest level is mostly just a move > predictor, yet it still wins well over 80% of its games. > > In my experience, when the strength difference is big, and the game is > even, it is usually enough for the strong player to only play good shape > moves. The move predictors only break down in complex tactical situations > where some form of look-ahead is critical, and the typical shape-related > proverbs provide wrong answers. > > Erik > > On Mon, Dec 15, 2014 at 12:53 AM, Hiroshi Yamashita <y...@bd.mbn.or.jp> > wrote: >> >> Hi, >> >> This paper looks very cool. >> >> Teaching Deep Convolutional Neural Networks to Play Go >> http://arxiv.org/pdf/1412.3409v1.pdf >> >> Thier move prediction got 91% winrate against GNU Go and 14% >> against Fuego in 19x19. >> >> Regards, >> Hiroshi Yamashita >> >> _______________________________________________ >> Computer-go mailing list >> Computer-go@computer-go.org >> http://computer-go.org/mailman/listinfo/computer-go > > > _______________________________________________ > Computer-go mailing list > Computer-go@computer-go.org > http://computer-go.org/mailman/listinfo/computer-go >
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