[Computer-go] ieee aticle about computer go by Jonathan Schaeffer

Darren Cook darren at dcook.org
Thu Jul 3 00:00:37 PDT 2014

> "Determining the best move is tricky, however. The most natural
> approach would be to pick the move with the highest probability of
> leading to a win. But this is usually too risky. For example, a move
> with 7 wins out of 10 trials may have the highest odds of winning (70
> percent), but because this number comes from only 10 trials, the
> uncertainty is high. A move with 65,000 wins out of 100,000 trials
> (65 percent) is a safer bet. This suggests a different strategy:
> Choose the move with the largest number of wins. And this is indeed
> the standard approach."
> Really? Changing the example, what if the 65,000 wins were out of
> 650,000? (1% win rate vs. 70% win rate), then does it always make
> sense to choose the path with the most number of moves?

If you had a choice between a 1% 65,000-wins move and a 70% 7-wins move,
MCTS will keep exploring the 70% move, until it either reaches 65,001
wins, and can be chosen, or the winning percentage comes down to 1% also.

BTW, that implies it would be very difficult to ever reach the situation
you describe, as 1% win rate moves wouldn't be given 650,000 trials
(unless all other moves on the board are equally bad, i.e. the game is
clearly lost).


Darren Cook, Software Researcher/Developer
My new book: Data Push Apps with HTML5 SSE
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