Thank you Jason and Don, Yes, that is my point Don, for example, after one simulation for each node, the nodes that won have now the same score, but I think that there is a lot of them, so how could I say that one node is better than another ?
Att, Santos, Gabriel. On Mon, Apr 1, 2013 at 2:16 PM, Don Dailey <[email protected]> wrote: > > > On Mon, Apr 1, 2013 at 1:10 PM, Jason House > <[email protected]>wrote: > >> On Apr 1, 2013, at 11:10 AM, "Gabriel .Santos" <[email protected]> >> wrote: >> >> > Hi! >> > >> > I'm a new computer-go researcher and I'm not a Go player. In order to >> get better knowledge of Go Game I would like to ask some questions about it >> (I know the rules of the game, I'm just not a good player). >> > >> > 1 - in order to evaluate simulations in MC. Is there any connections >> between the type of moves made in the game ? For example, if i take two >> simulations, victory in both simulations, in one of them I had just one >> nakade move and in the other one I had 5 nakade moves. Could I say that >> simulation two is better than simulation one ? By better i mean is it more >> worth that I take more time simulating the states from the second >> simulation instead of the first one ? >> >> I do not know of any engines that differentiate between the quality of >> simulations, only the result. The investment in a particular tree node is >> based on the win rate, the rave win rate, and bias with priors. >> > > Of course that's no reason not to try it but it seems like it would be a > really difficult proposition. If I understand this I think the point is > that perhaps there is more relevant information contained in one playout > over another and somehow it might be possible to take advantage of that? > > > Don > > > > >> >> >> > 2 - So, in this way could I conclude that, for example, Nakade moves >> are ALWAYS better than Atari Defense Moves ? >> >> I think there are very few black and white rules about which heuristic is >> better than another. There are a few different approaches to use heuristics >> inside a playout. Most are statistical. >> >> >> > 3 - As far as I know the alpha-beta approach has not succeeded due to >> the inefficiency of the evaluation functions known. So,where do you guys >> think that lies the future of Computer-GO ? MC methods ? The classic >> approach on board games ? (Minimax, Neural Networks, etc). >> >> MC is definitely the future. I think there are ways to blend classic >> methods with MC methods, but most are still experimental. >> _______________________________________________ >> Computer-go mailing list >> [email protected] >> http://dvandva.org/cgi-bin/mailman/listinfo/computer-go >> > > > _______________________________________________ > Computer-go mailing list > [email protected] > http://dvandva.org/cgi-bin/mailman/listinfo/computer-go >
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