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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