Author: James Swafford
Date: 16:45:57 08/05/02
Go up one level in this thread
On August 05, 2002 at 18:01:57, Vincent Diepeveen wrote: >On August 05, 2002 at 17:58:16, James Swafford wrote: > >this is in my alltime CCC posting. > >If a value from +3 goes to -3 then that's the most absurd thing >which can happen. Of course, if you're implying -3 means "it's good to get rid of the bishop" or some other similar nonsense. But the point is that moves are more important than eval term weights. Surely you agree... (?) > >>On August 05, 2002 at 17:53:09, Sune Fischer wrote: >> >>>On August 05, 2002 at 17:18:24, Vincent Diepeveen wrote: >>>>>It will adjust _all_ the weights that make a contribution in the eval. >>>> >>>>I know, but that is exactly the problem. >>>> >>>>*that* isn't working, because it doesn't know what it is adjusting, >>>>so it doesn't draw the right conclusions at all. in fact an infinite >>>>run of TD learning will only by random luck manage to find out >>>>what a good parameter set it, that's exactly the problem here. >>>> >>>>Drawing conclusions in chess is a problem anyway, because results of >>>>a game are not always determined whether something is better or worse. >>> >>>I don't think you understand what TDLeaf is doing at all, which is why you are >>>so pessimistic about it, to you it is a mysterious black box and you don't >>>believe in this 'magic'? >>>Maybe I'm too optimistic, but I won't toss it out the window without trying it. >>>I think very few people with the required knowledge in _both_ areas have tried >>>it. >>> >>>> THE REAL PROBLEM IN CHESS: >>>> >>>>Suppose it happens that the tuner (TD neural network or whatever >>>>as long as it's optmizing a bunch of parameters at the same time) >>>>have by accident chosen a starting set where >>>>*all* my parameters tuned very well except the open file >>>>bonus. Instead of positive +0.50 it has put it to negative -0.50. >>> >>>That would be an interesting challenge for the finished tuner, and I will do the >>>test when I can. But I just don't see how you can ever conclude you have very >>>well tuned parameters, in fact this is _the_ problem we are trying to solve. >>>I would judge it by its strength only, if it achieves higher rating than before, >>>then it's stronger and I would conclude that the values before where _not_ >>>optimal. >>> >> >>Well put, and I totally agree. The values themselves have absolutely >>no significance. What matters is the move that's produced. >> >> >>>>In nowadays chess that means a sure defeat. >>>> >>>>So the learner will draw the wrong conclusion, because the >>>>next run where it tunes another x parameters wrong, the randomness >>>>of the position makes the defeat less sure. >>>> >>>>It is a trivial fact in chess that if you play for random positions that >>>>the chance you win or draw is bigger than with a good program with one >>>>huge problem, because the randomness of a position is having a small >>>>chance to confuse the opponent, because the loss by natural induction doesn't >>>>apply. >>>> >>>>to explain this: if 2 players try to achieve nearly 100% the same thing then >>>>obviously if 1 thing is completely *dead* wrong, you lose chanceless. >>>> >>>>If 2 programs are *completely different* from each other then this chance >>>>is less. >>>> >>>>It's here where it is clear that domain dependant knowledge is required. >>>> >>>>No if you make an autotuner you are not going to 'guide' it each run of it. >>>>you have a tuner out of LAZYNESS. Because you can do yourself a better job >>>>anyway. >>> >>>I can't say if a rook is 5 pawns, 5.2 pawns, 5.6 pawns. Is a double pawn 0.8 >>>pawns or 0.73 pawns etc.? >>>I have no idea what values to give, not even to 0.3 pawn accuracy. >>> >>>If I setup a position and the static score is off, of those 41 parameters that >>>contribute, how do I find those that matter and how much to adjust them? >>> >>>-S.
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