Re: computer-go: Machine Learning and Go

[Serge Boisse <boisse@xxxxxxxxxxxx>]

Thore Graepel wrote:
> 
> Hello everyone,
> 
> we have just completed the first stage of our project "A machine
> learning approach to the game of go" and have summarised the results in a
> little paper.  You can download the paper from
> 
> http://stat.cs.tu-berlin.de/~guru/publications.html
> 
> It is called "Go, SVM, Go" and we submitted it to NIPS2000.
> 
> I thought you might be interested in this because we use a new approach of
> converting a graph-based representation into feature vectors that enable us
> to learn some aspects of good and bad "shape". These methods are then used to
> learn good Tsume-Go moves and to play on a 9x9 board after learning from game
> records. I would be delighted to receive some comments and suggestions. Also, if
> you have any questions, please feel free to ask.

Nice work !

But CFG do not capture some importants aspects of go, eg. influence and spacial
relationships. 
consider the  beginning of the game, with, say, just 2 stones (one of each
color) on the board. We know that the rest of the game will be very different if
the first (black) stone is for instance at 4-4, 3-4, 3-5 or 4-5 point. The white
response might also be put at the oposite diagonal corner, or at an adjacent
corner, and the remaining game will again be very different. All of these have
same CFG, however :

	w---.---b

So i think there's "something" to add to CFG to represent these differences : we
must find a way of representing the spacial relationships btw chains,
territories, and board limits. But how ? the only clue i can think of is that
this must be done in a symetry-independant way...

 
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