Re: [computer-go] Purpose of Neural-Nets?

["Nicol N. Schraudolph" <compgo@xxxxxxxxxxxxxxx>]

> In my own field, computer-chess, there are no real efforts to build
> a neural-net programm. In Go neural nets seem to be an inportant
> concept. Can the neural-net experts explain me the purpose?

Here's my summary:

The attraction of go for machine learning is that it is a finite, discrete
domain with rather nice properties (zero sum, perfect information, etc.)
that nonetheless has proven very complex and challenging.  The attraction
of machine learning for go is that (unlike chess) nothing simpler has
been found to work all that well yet.  There are reasons to believe
that neural nets may be useful for some (but not all) aspects of go.
Historically, neural net go was inspired a decade ago by the success of
TD-Gammon; it is becoming more popular now as we reach the large CPU
speeds, memory, and training data needed to have a serious go at it.

> In case of a). One could first learn the weights and then hardcode the
> weight-function to speed up the evaluation. Alternatively one could have 2
> versions of a program. The slow learner and the fast tournament-player. Is
> this done, or is a neural-net programm always a neural-net program?

In software I doubt this is necessary - neural net evaluations can be
made very fast regardless of whether the weights are constants or not.
Carrying the training code around with you carries virtually no speed
penalty, as long as you don't invoke it.  (If you want to use special
hardware for evaluation it's a different story.)

Best wishes,

-- 
    Dr. Nicol N. Schraudolph                 http://n.schraudolph.org/
    Steinwiesstr. 32                         mobile:  +41-76-585-3877
    CH-8032 Zurich, Switzerland                 tel:      -1-251-3661

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