Darren,

>> I imagine it would be fairly easy to swap from MCTS to a CGT solver once it
>> could be applied.. Or is this not interesting for some reason?
>
>It only becomes usable once the game is fairly much decided. (Though,
>you can construct artificial positions where it gives you a correct move
>is non-obvious (*) ) IIRC Martin's papers tried to extend it back a bit
>earlier in the game, and that is the state of the art as far as I know.
>
>In contrast MCTS, while theoretically only giving you a rough estimate,
>is in fact giving you a Good Enough estimate of the score by the time
>CGT could be used.

I don't think MCTS is good enough at endgames.  Actually, its 
performance at endgames is worse than middle, because IMHO MC 
simulations don't evaluate the values (due to execution speed) of 
yose-moves and play such moves in random orders.  Assuming there are 7, 
3, 1 pts moves left at a end position, for example.  Correct order is 
obviously 7, 3 and 1 (sente gets +5 pts) but all combinations are played 
at the same probability in MC simulations now.  The average of the 
scores has, thus, a big error.  

A related problem is the understanding of the end of the game.  
Strong human players easily detect it but bots play much more moves in 
playouts.  If not, the best yose sequence might be found in search 
tree.

Lukasz Lew did some work in this direction; Chapter 5 of his thesis.
http://www.mimuw.edu.pl/~lew/files/phd.pdf

Hideki

>Darren
>
>*: http://senseis.xmp.net/?MathematicalGo
>
>I guess you've already read Chilling Gets the Last Point ?  There is
>also a lot on Sensei's Library:  http://senseis.xmp.net/?CGTPath
>
>I spent some time trying to extend the ideas, when first learning of
>CGT, and got nothing worth reporting. They did inspire some of the work
>I did on life and death analysis; it was intellectually stimulating, but
>I kept coming back to the same core issue: you need to do search, to
>handle the kind of situations that come up in real games.
-- 
Hideki Kato <mailto:[email protected]>
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