Author: Vincent Diepeveen
Date: 14:59:04 08/05/02
Go up one level in this thread
On August 05, 2002 at 17:53:09, Sune Fischer wrote: Play 500 games with TD and see how optimistic you are then. Or make a huge evaluation and toy a lot with book learning which is a similar problem. >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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