Hi Rémi, Thank you for this paper. I found the work very interesting, well written, and the paper is clear and pleasant to read. As two things are modified in the same time (simulation policy and tree search), I wonder what is the contribution of each part in the overall improvement. For example I wonder what is the exact improvement of the new policy simulation on itself (without modifying UCT) over the one of MoGo. I guess you already have those results, but don't have enough room to put it on this paper. For example, if I remember correctly plain UCT with MoGo's simulation policy at 70k per move was 83% against gnugo 3.6. What is the result with your simulation policy? 85%/90%/95 %/ more? That would help to know where this approach is more usefull: simulation, tree or even both. I mean it is possible that combining the two improvements makes a stronger player that taking the sum of each improvement. If so, that would mean that some win-win effect arises, and the tree search part type has to be related to the simulation type.
Again, very interesting work. Sylvain 2007/5/17, Rémi Coulom <[EMAIL PROTECTED]>:
Hi, I first thought I would keep my ideas secret until the Asmterdam tournament, but now that I have submitted my paper, I cannot wait to share it. So, here it is: http://remi.coulom.free.fr/Amsterdam2007/ Comments and questions are very welcome. Rémi _______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
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