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Subject: Re: Multiple Linear Regression - Secret of the Commercial Chess Programm

Author: Frank Phillips

Date: 04:59:43 04/30/04

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On April 30, 2004 at 07:11:02, rasjid chan wrote:


>
> But still as others posted, and from theory of alpha-beta that it relies
> only on one signal, ie eval(), evaluation determines how alpha-beta
>searches and the returned best-move.

Sure, chess is a zero sum game with only three outcomes: 1, 0 or ½.  So, unless
the search sees to the end of the game then an evaluation is needed to chose an
end point.  I suppose, in fact, to differentiate between all endpoints that are
not won, lost or drawn.  I view the evaluation (and chess theory) as a predictor
of the likely outcome of a position for which the end result is unknown.
Sometimes the prediction will be wrong, even with best play.  Presumably due to
tactics or wrong or inapplicable theory or the evaluation function being a poor
predictor in the specific case.

The question then is how much of an evaluation do you need to do well against
the opponents you are likely to play eg to take you to a particular level.
There will always be occasions where the presence of specific knowledge hurts
(over-valuing some positional factors that are not as important as others in
specific positions or vice versa) as will the abscene of specific knowledge in
particular circumstances.

In theory search or egtbs (the same as search but pre-computed backwards) must
dominate all else.  Fortunately chess is still interesting because from a
practical point of view it cannot.

I doubt there is single set of killer positional elements and their weighting
factors.  Nor a single way to arrive at a decent set.  And I would repeat that
on a modern cpu a standard search plus any sensible evaluation function should
take you way above 1900.





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