Author: Vincent Diepeveen
Date: 15:01:57 08/05/02
Go up one level in this thread
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. >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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