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. > 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
