[computer-go] Re: Weights are not very important was: ANNs aspotentially useful in the computer goproblem

["chrilly" <chrilly@xxxxxxxx>]

Waldviertel-Hochland, 11.01.2004
The quality of an evaluation function is according my experience in
computer-chess determined by:
1) The number and the quality of positional features
2) The accuracy with which these features are classified, especially the
percentage of wrong-classifications.
3) The fine-tuning of the weights.

According my experience is 1) and 2)  much more important than 3). Logically
one can not fine-tune a feature, when the programm does not know at all
about it. It is also pointless to fine-tune the weight, when the feature is
in a high percentage of positions mis-classified.
E.g. it is in chess important to have some knowledge of center-centrol. The
formal defintion, recognition in a programm, of center control is a very
difficult problem. E.g. GM J.Nunn writes: "There is no mathematical
definition of this term, but one usually sees it". Unfortunatly programms
have no chess-eye.
Once one has implemented the right features and can classify them - most of
the time -correctly, the setting of the weights can be in a relative wide
range without affecting the playing strength of the programm in a
significant way. The programm plays with different weights different, but it
is astonishing, how little impact this has on the Elo-rating.

I think the same should hold in Go. I assume it is much more important to
recognize groups correctly or the divide the game in the correct subgames,
than optimizing the weights. I also assume that the flat gradient - changing
weights has only a marginal impact on performance - is also a serious
problem for an automatic learner. I assume from my hand-tuning experience,
that there are also a lot of local optimums.

Note: I assume of course, that the weights are already in a more or less
optimum range. The flat gradient "law" is only true around this optimum.

Best Regards
Chrilly

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