http://stat.cs.tu-berlin.de/~ralfh/go.ps.gz
Thore Graepel, Mike Goutrie, Marco Krüger, and Ralf Herbrich used an SVM to
predict moves from pro games; it was particularly successful for predicting
opening moves, as I recall.
Terry McIntyre <[EMAIL PROTECTED]>
They mean to govern well; but they mean to govern. They promise to be kind
masters; but they mean to be masters. -- Daniel Webster
----- Original Message ----
From: Erik S. Steinmetz <[EMAIL PROTECTED]>
To: computer-go <computer-go@computer-go.org>
Sent: Monday, October 15, 2007 11:53:25 AM
Subject: [computer-go] Opening game strategies
Greetings all,
I have been looking through the literature (many thanks to Markus's
wonderful online bibliography) on existing strategies in the opening
game, and have not found too many articles on the specifics outside
of a few papers on neural net learning applied to the opening. There
are some vague references to 'pattern matching' to generate moves,
but no information about how those patterns and moves were created.
I am wondering if anyone knows of any attempts made to run pattern
recognition (for example, clustering) algorithms over a library of
games in order to learn reasonable opening moves. If so, and there
are any papers about the success (or failures) of such an effort, I
would really appreciate a pointer!
Many thanks in advance for any info,
All the best,
Erik Steinmetz
[EMAIL PROTECTED]
[EMAIL PROTECTED]
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