Finally I use a pruning method I have been using with non-MC programs where moves evaluated bad at ply n is pruned when they are evaluate again at ply n + 2 and their local neighborhood has not been changed. This method is a little crude and perhaps a little risky, but the gains clearly outweighs the disadvantages.
I tried something similar while not totally pruning the move. Simply takes the statistics of one parent to initialize the UCT statistics. It was of little help in 9x9. I did not try in 19x19. But when it is very little time *left* to search I
think it is a waste of time to spend effort on the moves that are the worst since they seem very unlikely to become the best move before we run out of time (at some point in time it is even impossible). I think the ideal algorithm should be sensitive to the time left and start to prune moves accordingly. UCT already does this to some extent, but I believe it does so only with very long thinking times.
I think it should be easy, given a UCT tree and the time left, to compute if one move has a chance to become best before the end or not. However, I think it should be more usefull to build an adaptative time control, rather than trying to improve UCT with hard time control. Sylvain
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