3 ms·

Current Monte Carlo programs have major problems taking advantage of increased processing power, so your major assumption is flawed. I don't have a citation at

Current Monte Carlo programs have major problems taking advantage of increased processing power, so your major assumption is flawed. I don't have a citation at hand, but there has been discussion of this on the computer go mailing list. It's not out of the question that the engines could be tweaked to improve their performance more with better/more hardware, but right now, they have disappointing results.

gcp 13y ago
I don't have a citation at hand, but there has been discussion of this on the computer go mailing list. You should post it. I suspect you misunderstood. Additional computing power doesn't help some problematic situations, but the overall strength still goes up nicely. There are also some problems with parallel scaling not actually improving raw performance, but this is equivalent to the speed not actually going up.
- hyperpape 13y ago
  Unfortunately, I can't seem to find it. I found other people on go forums saying that Remi Coulom might have said it, but not a citation. Also, just to be clear, the claim that I'm considering is that around 4-6 dan KGS, you start getting markedly lower payoff in Go strength for increasing the number of playouts. The big concern seems to be capturing races.
thangalin 13y ago
"Fortunately, MCTS lends itself much more easily to parallelization" and "Although MCTS can benefit from parallel implementation, this benefit is not always as large as might be hoped" as no game-tree is constructed.[3] I think the assumption holds: we won't have to wait 15 years; in ~7 years (or fewer) we will have 9p Computer Go players. [1] https://webdocs.cs.ualberta.ca/~mmueller/publications.html https://webdocs.cs.ualberta.ca/~mmueller/publications.html [2] https://webdocs.cs.ualberta.ca/~mmueller/ps/2013-CG-MCTS-Go-Limits.pdf https://webdocs.cs.ualberta.ca/~mmueller/ps/2013-CG-MCTS-Go-... [3] http://www0.cs.ucl.ac.uk/staff/D.Silver/web/Applications_files/grand-challenge.pdf http://www0.cs.ucl.ac.uk/staff/D.Silver/web/Applications_fil...

gcp 13y ago

I don't have a citation at hand, but there has been discussion of this on the computer go mailing list. You should post it. I suspect you misunderstood. Additional computing power doesn't help some problematic situations, but the overall strength still goes up nicely. There are also some problems with parallel scaling not actually improving raw performance, but this is equivalent to the speed not actually going up.

hyperpape 13y ago
Unfortunately, I can't seem to find it. I found other people on go forums saying that Remi Coulom might have said it, but not a citation. Also, just to be clear, the claim that I'm considering is that around 4-6 dan KGS, you start getting markedly lower payoff in Go strength for increasing the number of playouts. The big concern seems to be capturing races.

thangalin 13y ago

"Fortunately, MCTS lends itself much more easily to parallelization" and "Although MCTS can benefit from parallel implementation, this benefit is not always as large as might be hoped" as no game-tree is constructed.[3] I think the assumption holds: we won't have to wait 15 years; in ~7 years (or fewer) we will have 9p Computer Go players. [1] https://webdocs.cs.ualberta.ca/~mmueller/publications.html https://webdocs.cs.ualberta.ca/~mmueller/publications.html [2] https://webdocs.cs.ualberta.ca/~mmueller/ps/2013-CG-MCTS-Go-Limits.pdf https://webdocs.cs.ualberta.ca/~mmueller/ps/2013-CG-MCTS-Go-... [3] http://www0.cs.ucl.ac.uk/staff/D.Silver/web/Applications_files/grand-challenge.pdf http://www0.cs.ucl.ac.uk/staff/D.Silver/web/Applications_fil...