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Subject: Re: chess and neural networks

Author: Ralph Stoesser

Date: 03:08:14 07/04/03

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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.

Chess is very concrete, but for example Backgammon and Checkes (Chinook
[Schaeffer, 1997]) are very concrete games too. There the NN-learned evaluation
is successfull. In fact you can solve very concrete problems with NNs.

>
>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.

Real GM-like chess knowledge is very hard (if not impossible) to express in a
straightforward algorithmic way. From that point of view every classical
evaluation function is a fuzzy approach to GM-like chess knowledge. Yes, we have
many known rules from chess teaching books. These rules can be implemented
directly into the evaluation function. But what about the dependencies between
these rules and what about that part of GM knowledge, that is hard to express in
a classical evaluation function? (i.e. when a GM says: "I feel good in this
position, but I cannot accurately determine why I have this feeling"). Exactly
these kind of things could solve a NN based evaluation by learning from GM
games.

Ralph

>
>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






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