On 9, Jul 2007, at 8:37 AM, George Dahl wrote:
On 7/9/07, Erik van der Werf <[EMAIL PROTECTED]> wrote: On
7/9/07, George Dahl <[EMAIL PROTECTED]> wrote:
> I think this is what I want. Thanks! So I might have to repeat
this
> a few hundred times to actually get a legal position?
Are you aware that nearly all of these positions will be final
positions?
This depends upon the distribution function for w/b/e. The greater
the % given to empty the further from "endgame" you will be.
So I'll repeat my question: why do you need any of this? If you only
need final positions it's probably much better to take them from real
games, and if you actually need middle game positions you will have to
use a different procedure...
E.
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Won't the final positions be much more likely to be rejected since
they are much more likely to be illegal?
Probably, but again, it will depend upon the distribution function.
What is your claim about the distribution of the number of stones
on the board with this scheme?
I am hoping to use this method to help generate training data for
a learning system that learns certain graph properties of the board
that can also be computed deterministically from the board
position. I know that might sound crazy, but it is working towards
the eventual goal of creating feature extractors for Go positions.
By learning to map Go positions as an array of stones to Go
positions as graphs of strings (instead of just mapping them with a
hand coded algorithm) I can take intermediate results in the
learner's computation and use it as a feature for another learner.
Have you read Ken Friedenbach's thesis Abstraction Hierarchies: A
Model of Perception and Cognition in the Game of Go (UC Santa Cruz
1980)? From what you are saying it sounds like you should.
- George
Cheers,
David
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