[computer-go] Monte Carlo result as a feature for pattern matching

[Chris Fant]

I have considered the same idea in the past.  The biggest problem is
that with each position having N possible states (depending on the
granularity of your influence number), the pattern space explodes,
thereby making automatic pattern aquisition difficult.



On 7/8/06, Peter Drake <drake at lclark.edu> wrote:
> A number of researchers are using Monte Carlo Go as a way of
> estimating the value of a given board position.
>
> (For those of you not familiar with this technique, it involves
> playing random moves to the end of the game and then counting the
> score.  This is repeated many times and an average result taken.)
>
> Pattern matchers regularly look at certain features such as the color
> and edge/off-board status of each point within the pattern.  Some
> pattern matchers look at more sophisticated features such as the
> number of liberties a block has.
>
> We would like to have access to life/death and influence information
> in patterns.  For example, we would like to have patterns that say
> things like "if a weak friendly stone shares a liberty with a strong
> friendly stone, consider connecting them" or "if a point adjacent to
> one of my stones is strongly influenced by me, playing on that point
> is probably not important".
>
> The problem is that open-ended life checkers and influence detectors
> are quite difficult to write.
>
> I propose (and am working on) using Monte Carlo analysis to find the
> expected ownership of each point on the board (say, as a number in
> the range [-1.0, 1.0]) and offering this information as a feature in
> the pattern detector.
>
> Thoughts?
>
> Peter Drake
> Assistant Professor of Computer Science
> Lewis & Clark College
> http://www.lclark.edu/~drake/
>
>
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