[computer-go] producing a good probability distribution over legal moves

[Don Dailey]

On Thu, 2007-05-17 at 12:17 -0400, George Dahl wrote:
> Imagine if you had a monte carlo program that took almost no time to
> run.  You would use it to do "heavy" playouts for another monte carlo
> program to make it even stronger. 

I tried something like this as a test with simple monte carlo.   I
called it recursive monte carlo.

This was not UCT, it was just the simple statistic monte/carlo where you
choose the single move that had the most success in totally random
play-outs.

This started when I noticed that even running 1 or 2 simulations gave
far better play than random.   So my idea was that instead of choosing
moves randomly in the play-outs,  I would choose each  move in the
playout by doing another set of play-outs - applying the same simulation
idea recursively.    

I didn't expect it to be fast enough to be practical, but I thought that
at least I could see if the idea was feasible.   If I'm doing 1000
play-outs, the version that does the sub-play-outs  should be far
stronger than the one that didn't even if it's a lot slower.

Instead, it was much slower and played worse.   I didn't pursue this any
farther, but I suspect that it should have worked.   I think the problem
is that I wasn't getting fair representation of each move.   The
sub-playouts were causing some moves not to get sampled much for
instance.  

I am guessing that if I used such a technique for the play-out portion
of the monte-carlo search, it would be incredibly strong for a given
level if I only considered the number of play-outs of the outer-level.

- Don