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Subject: Re: chess and neural networks

Author: Fernando Alonso

Date: 08:35:14 07/02/03

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On July 02, 2003 at 04:17:28, Rolf Tueschen wrote:

>On July 01, 2003 at 18:20:12, Fernando Alonso wrote:
>
>>On July 01, 2003 at 15:46:06, Ralph Stoesser wrote:
>>
>>>Right, that's what I intend with my question. During a 'normal' chess game a
>>>chess engine has to face often positions where the difference in evaluation
>>>between let's say the 5 best moves or so is very small. In such circumstances a
>>>trained neural network maybe could help to find good positional moves better
>>>than a classical evaluation.
>>>
>>>Ralph
>>
>>I agree with you, that is the important point. To put it in other words, can I (
>>a patzer) with my little chessknowledge, beat Fritz 8 using Fritz 8 to analyze
>>the moves I my brain "thinks"?. I am sure there is a level of playing were
>>someone using a program can beat easily the same program playing alone. But can
>>that knowledge be implemented in neural networks?
>
>
>What knowledge? For the moment nobody addressed Tom's objection IMO. Chess is
>very concrete.
>
>Now what you all are saying that there existed a "knowledge" to find "good"
>positional moves. Of course our human GM have that knowledge. It is a mixture
>out of the evaluation of the very concrete position, deeper (later) consequences
>and again very concrete calculations for these _later_ positions. I dont see why
>"fuzzy" approaches should do that job better than the "classical" evaluation.
>
>What you in special are proposing is NOT a question of "knowledge" but simply
>one of cheating. You know exactly the "thought process" of a program. So you can
>always discover a difference in the evaluation of the final position. Now the
>trick is to invite the machine to go blindly for a big difference which is then
>the win for you. This is typically the approach of smart amateurs with weaker
>chess talents. [Dreihirn comes to mind.] But real chess is something else. A GM
>does NOT win because he's a clairvoyant but because his judgement (combining the
>very concrete with the general experience for the actual and then later
>positions) is "better". A weaker chessplayer has no adaequate judgement at all.
>I cant see why neural networks should have one - where should it come from? Out
>of the blue?
>
>Again, you simply didn't address Tom's objection that "sometimes" it is very
>important where your Rook is standing. Very concrete. How to handle that
>"sometimes" it is "important"?
>
>Rolf

First of all I would like to excuse my poor english or my lack of adecuate
judgement and my weak chess talent, that has probably caused some
misunderstanding. :)
I have never ment anything like cheating. What I had in my mind was the idea of
Advanced Chess played in Leon for some years, where both players had a computer
to assist them. The games were of high quality and the match very interesting.
And the concept of that way of playing is what I found interesting. A GM to plan
and a computer to calculate. Reaching the understanding of the game that a GM
has it is imposible for a program nowadays, that is clear, but the tactical
ability is something some programs have reached and surpased humans in many
cases. I was just wondering how much of that understanding of the game is needed
to beat an opponent with the same tactical ability, that is to say, the same
program. I believe programmers do this to improve their engines, try to keep the
tactical strengh of their programs and at the same time improve their chess
knowledge, by "clasical" methods. This is not cheating and I donīt understand
why trying to reach the same goal through neural networks should be called
cheating. I am just giving a thought, I am not saying this is the answer to
chess programing but a wide field to investigate and combine both techniques.

Neural networks solve very concrete math problems better than conventional
programming, for example finding shortest path between a number of points in
three dimensions. The problem is concrete, the solution aswell, and the way we
approach the problem is "fuzzy" if we want to solve it with a computer when the
number of points is to high. It is only an example, again Iīm not saying
everything can ve solved with neural networks but it might be worth giving it a
try.

Yes, sometimes is very important where your Rook is standing, but how knows if
the combination of a top program with some other new technique would help us to
handle that better. I believe many progress in science has been made attacking
the problems from a completely different point of view to the classical one and
combining techniques used in other fields of science but very little tested in
that particular one.

Best,
Fernando Alonso.



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