thank you for sharing the paper. "the Maximum Frequency method is based on the maximization of the difference between the expected reward of the optimal move and that of others"
intuitively it feels that biasing random search towards the optimal route would yield reduced failure rates, yet it does seem to depend on knowing what the optimal route is beforehand. if i knew the optimal route to get from A to B, i wouldn't bother doing a random search, but just follow it. "This property [“bias in suboptimal moves”] means that the impact of missing the optimal move is much greater for one player than it is for the opponent." i find this conclusion puzzling because Go is a zero-sum game, so what is good for one side is equally bad for the other, not variably so. I have not checked the statistical inference calculations to see whether there is an error in them.
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