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Subject: Re: Hello from Edmonton (and on Temporal Differences)

Author: Vincent Diepeveen

Date: 15:01:57 08/05/02

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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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