On Mon, 2008-08-11 at 20:10 -0700, David Fotland wrote:
> Sorry, but I can’t let this statement go past.  The go programs in the
> 90s did local search, but not much global search.  For example Many
> Faces did a one ply global search, with a variable depth quiescence
> search.  I added an alpha-beta search to Many Faces last year, and it
> made a huge improvement in strength.  So it is not true that
> alpha-beta pruning hit a roadblock.
> 

I never doubted alpha-beta but when you say alpha-beta and GO in the
same sentence, people automatically believe the program is going from
99% evaluation to 1% evaluation and 99% stupid.  In fact you are still
spending most of your time evaluating positions.   

I'm still not convinced that you can't make a strong alpha beta GO
program if you have some imagination.  It cannot just be a converted
chess program, it has to be different, but still alpha beta at heart.
It would have to be extremely selective.  

- Don




>  
> 
> For me, the big advantage of UCT/MC is that it eliminates the huge,
> slow, buggy evaluation function.  Simple playouts are much much easier
> to make bug free.  Bugs in the evaluation function caused many losses,
> and are almost impossible to eliminate in traditional programs, since
> the evaluation functions are so complex.
> 
>  
> 
> David
> 
>  
> 
>  
> 
> It seems that alpha/beta pruning hit a roadblock a long time ago in
> go. Now we have MC... which as you increase the number of samples..
> you start to get closer to an equivalent alpha/beta. But... there are
> still huge groups of samples that are not checked... and if you want
> to somehow prove you have the best move... how will you do it? Will
> you make the sample size equivalent to the number of possible samples?
> How will you do this practically? You can only state with a certain
> confidence that you did make the best move and this would be bound by
> our computational resources.
> 
> 
> 
> 
>  
> 
> 
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