Author: Uri Blass
Date: 01:35:12 03/11/04
Go up one level in this thread
On March 11, 2004 at 04:23:04, José Carlos wrote: >On March 11, 2004 at 04:04:01, Uri Blass wrote: > >>On March 11, 2004 at 03:08:36, José Carlos wrote: >> >>>On March 10, 2004 at 18:29:31, Uri Blass wrote: >>> >>>>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. >>>> >>>>The question is how much time is long enough. >>>>It is clear that there is a way to find the best setting after enough time. >>>> >>>>Even the simple way of testing every possible setting can find the best setting >>>>if you only have 10^1000 years to wait. >>> >>> >>> The number of possible settings is infinite, so this method can't be sure to >>>find the best settings. >>> >>> José C. >> >>No >> >>The number of possible setting is finite but very big and if you have 200 >>parameters and everyone of them can get 10 values you have 10^200 setting. >> >>I admit that practically you probably have thousands of numbers when most of >>them can get more than 10 values so you have near 10^10000 possible setting so >>you nead probably at least 10^10000 years to find the best setting by testing >>all of them and 10^1000 is not enough. >> >>It does not change my point in this discussion that the question if you find the >>best setting if you wait enough time is not important and in this case waiting >>until programs solve chess may be shorter wait. >> >>Uri > > Uri, the number is infinite because parameter values are integer and integer >numbers is a set with infinite elements. Not in programming. Integer numbers has finite number of values. only 2^32 values. > Besides, the "given enough time" concept implies a convergency, and thus an >iteratively better results (not necessary increasing accuracy all the time but >increasing as a tendency). Your "try all settings" has no convergency, that's >the difference. > > José C. You also can have convergence. You start by testing setting 1 and setting 2 against many opponents. You decide based on the results if setting 2 is better than setting 1. You can always have best setting so far that you compare with another setting that you still did not test. Uri
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