On Sep 28, 2008, at 7:05 AM, Claus Reinke wrote:
For instance, if an intersection belongs to the same colour in
all playouts,
chances are that it is fairly secure (that doesn't mean one
shouldn't play
there, sacrifices there may have an impact on other
intersections).
Or, if an intersection is black in all playouts won by black,
and white in
all playouts won by white, chances are that it is fairly
important to play
there (since playouts are random, there is no guarantee, but
emphasizing
such intersections, and their ordering, in the top-level tree
search seems
profitable).
We (the Orego team) have done some work along these lines this
summer. We're working on a paper.
Secondly, I have been surprised to see Go knowledge being applied
to the
random playouts - doesn't that run the danger of blinding the
evaluation
function to border cases?
Yes, but you try every move in the actual search tree (unless you
have a VERY safe exclusion rule, such as "don't play on the extreme
edge of the board unless it's within 4 points Manhattan distance of
an existing stone").
Thirdly, I have been trying to understand why random playouts work
so well for evaluating a game in which there is sometimes a very
narrow
path to victory. Naively, it would seem that if there was a
position from
which exactly one sequence of moves led to a win, but starting on that
sequence would force the opponent to stay on it, then random playouts
would evaluate that position as lost, even if the forced sequence
would
make it a win.
It's true, this is a problem; raw Monte Carlo fares poorly at reading
ladders.
Peter Drake
http://www.lclark.edu/~drake/
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