Author: Robert Hyatt
Date: 07:47:41 06/27/01
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On June 26, 2001 at 23:35:15, Uri Blass wrote: >On June 26, 2001 at 23:06:26, Robert Hyatt wrote: > >>On June 26, 2001 at 14:29:25, Uri Blass wrote: >> >>> >>>It also cannot be repeated against a chess program if it remembers the game and >>>has learning by position or if it is not deterministic. >>> >>>Uri >> >> >>This doesn't work quite like you think. For lots of well-known reasons. The >>most important is that if you go out of book very early, and don't see anything >>bad happening for a long while, it will take a _long_ while to propogate those >>scores back up the search tree to avoid a bad early move that doesn't lose for >>(say) 20 more moves. > >You can learn to remember scores of 0.1 or 0.2 pawns lower than the scores of >the game after losing so if the program has a logical alternative it is going to >choose it As I said, it doesn't work like that. Suppose that the first move looks like 0.00 when you search it. And all the others look like -.3 at the same depth. Your -.2 score will _still_ be the best, since you can't see deep enough to discover that one of the -.3 moves will become +.3 in 4 more plies. That is a "local maximum" and you can't work your way around it > >For example if it lost by 1.e4 c5 2.Na3 Nc6 when the score was 0.24 for itself >After 2...Nc6 then it may remember only 0.04 for itself based on the fact that >it lost the game and it may help it to prefer 2...e6 that gives it 0.17 pawns >adavnatge for black. > >Uri But suppose all the others are < .04? you are stuck there.
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