Author: Dave Gomboc
Date: 13:52:01 03/10/04
Go up one level in this thread
On March 10, 2004 at 16:04:32, Dann Corbit wrote: >On March 10, 2004 at 14:41:47, Uri Blass wrote: > >>On March 10, 2004 at 14:23:29, Dave Gomboc wrote: >> >>>On March 09, 2004 at 16:05:15, Gian-Carlo Pascutto wrote: >>> >>>>Yet no top program does this, and they had a human correct it >>>>afterwards in Deep Blue. The conclusion should be obvious. >>> >>>Is that so? >>> >>>>If you can develop a *top level* evaluation function, better than >>>>good human tuning, solely on learning from a GM games database, >>>>you deserve an award. Nobody has succeeded before. >>> >>>Jonathan Schaeffer learned weights in Checkers [Chinook] without even using a >>>human games database (he used TD learning). The weights he tuned score 50% >>>against his hand-tuned code. >>> >>>I learned weights in Chess [Crafty] using 32k positions, hill-climbing an >>>ordinal correlation measure. It too scores 50% against the hand-tuned code. >> >>How many games and what time control? >>There is a difference if you score 50% with 2 games and with 2000 games? >> >>It is also possible that you get 50% against Crafty but less against other >>opponents. >> >> >>>Given Deep Sjeng's source code, I could zero its evaluation function weights, >>>and learn them from GM games to score 50% against the weights you have right now >>>too. >> >>You may be right but you cannot know about source code that you do not know. > >With his method, he will eventually reach a good result with any engine. >It uses generations, and discards the weaker ones absorbing the stronger ones. >After long enough waiting, it must become stronger. > >He wrote a paper on it. >Look at this: >http://www.cs.ualberta.ca/~dave/ >to find this: >Gomboc et al. Ordinal Regression for Evaluation Function Tuning, 10th >International Conference on Advances in Computer Games, Graz, Austria, Nov. >24-27, 2003. The work has progressed further, also. Newer stuff will be in my M.Sc. thesis, which will come out soon. Dave
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