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Subject: Re: Automatic Eval Tuning

Author: Landon Rabern

Date: 10:27:57 06/29/01

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On June 29, 2001 at 11:18:48, Vincent Diepeveen wrote:

>On June 29, 2001 at 11:14:34, Artem Pyatakov wrote:
>
>>I am curious, have people here experimented or extensively used Eval Function
>>tuning based on GM games for example?
>>
>>If so, is it effective to any extent?
>>
>>I came across this page and it seemed kind of interesting:
>>http://www.tim-mann.org/DT_eval_tune.txt
>>
>>Have others tried this too?
>>
>>Thanks in advance.
>
>yes i tried some years ago automatic tuning.
>
>The bigger your evaluation is, the more problematic tuning it automatic
>is. Also automatic tuners don't have any chess knowledge, so they
>don't see the difference between tuning passed pawns negative if you happen
>to have a testset where a passer is bad now and then.
>
>Another problem for automatic tuners is that you tune for testposition set X,
>but that in reality it has to work well also for testset Y where it has
>not been tuned for.
>
>Evaluations hand tuned take into account testset Y, not only testset X.
>
>Anyway, when your number of parameters gets quite a big number then
>automatic tuning doesn't work anyway anymore.
>
>Of course it might beat random chosen parameters, but it'll never beat
>hand chosen parameters (unless a fool choses them).
>
>Best regards,
>Vincent

You are assuming that all you can do is supervised learning over a data set.
The method that shows the most promise is Reinforcement learning.  This allows
the learner to continuosly learn, if there is a hole in the evaluation it will
get fixed, because otherwise the program will lose.  You might want to try using
something like Q-learning or TD(lambda).  It might take a long time to get good
values from scratch, but you might have more success if you start from your
original hand coded numbers.

Regards,

Landon



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