Author: José Carlos
Date: 01:23:04 03/11/04
Go up one level in this thread
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. 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.
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