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 ? 2 - So, in this way could I conclude that, for example, Nakade moves are ALWAYS better than Atari Defense Moves ? 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). I know that it is a lot of questions, but in order to get a computer go machine to outperform a human player I think that the machine should to ratiocinate like a human player. P.S: Sorry about my bad english. Thanks in advance! Att, Santos, Gabriel.
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