Author: Sune Fischer
Date: 14:53:09 08/05/02
Go up one level in this thread
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. >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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