On Wed, Nov 25, 2009 at 2:00 PM, Matthew Woodcraft <[email protected]>wrote:
> steve uurtamo wrote: > > the way to do all of this exactly is with experimental design. > > > > to design experiments correctly that handle inter-term interactions of > > moderate degree, this tool is quite useful: > > > > http://www2.research.att.com/~njas/gosset/index.html<http://www2.research.att.com/%7Enjas/gosset/index.html> > > That doesn't seem to directly support deriving information from random > trials. For computer go tuning, would you play multiple games with each > parameter set in order to get a meaningful figure? That seems likely to > be less efficient than treating it as a bandit problem. > This does not replace bandit, it's a way to tune parameters. You might have 50 parameters and so you play a few thousand games using random combinations of these parameters for instance. And then you have data based on the win/loss records of the different parameters and use this to settle on a "good" set of parameters to be used. - Don > > -M- > _______________________________________________ > computer-go mailing list > [email protected] > http://www.computer-go.org/mailman/listinfo/computer-go/ >
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