I've just finished "Computer Go an AI Oriented Survey" by Bruno Bouzy and Tristan Cazenave. A very long survey, packed with so many details that I suspect I'll need to reread it in a week or two. As well as surveying a wide range of techniques used to tackle go, it also discusses some of the representations used by specific implementations.
There were quite a few interesting pieces about the end game, and I shall have to read some of the primary papers (which all seem to be listed in the very extensive bibliography).
I was surprised both at the lack of genetic algorithm approaches used with patterns (as opposed to neural networks) mentioned and also at the fact that all researchers seem to be opting for light weight patterns rather than complex patterns, both of which seem promising to me.
The final paragraph contains a very interesting explanation for the wide gap in perceived strength of go programs: that human player can learn the faults of a computer go player over a series of games, and systematically exploit them. Whereas a human player quickly learns in at least a shallow sense to fix faults as they are repeated exploited (or at least alters play to fight their exploitation), computer go players are static and cannot adapt themselves.
Computer Go an AI Oriented Survey: http://www.ai.univ-paris8.fr/~cazenave/CG-AISurvey.pdf
Home page for Bruno Bouzy: http://www.math-info.univ-paris5.fr/~bouzy/Go.html
Home page for Tristan Cazenave: http://www.ai.univ-paris8.fr/~cazenave/
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