> Let's take a basic example of a leaf node in an MC search tree that
> hasn't been expanded, but has 4 children. Let's say that random
> simulation through the children have winning percentages of {46%, 51%,
> 47%, 48%}. Assuming a uniform simulation policy, the winning percentage
> would be the average of the four, or 48%... but when that node gets
> expanded, it'll start discovering the winning percentages of the
> children nodes. Now when we look at the node that used to be our leaf
> node and ask what it's winning percentage would be, we come to a
> different conclusion... The winning percentage is either 46% or 51%
> depending on if the color to move.
>
> In this example, the difference between the 0-ply and the 1-play is 3%
> (51%-48%) or 2% (48%-46%) depending on what the color to move was.
I hope someone else will answer as I'm confused what you saying. In the
tree a node is either for black to move or white to move, but the
winning percentage is always for the side we are playing in the game. So
expanding out a node just increases the accuracy of the winning
percentage estimate at that node.
Darren
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