Computer Chess Club Archives


Search

Terms

Messages

Subject: Re: chess and neural networks

Author: Vincent Diepeveen

Date: 12:42:25 07/06/03

Go up one level in this thread


On July 06, 2003 at 08:00:48, Uri Blass wrote:

>On July 06, 2003 at 03:04:07, Christophe Theron wrote:
>
>>On July 06, 2003 at 01:15:41, Uri Blass wrote:
>>
>>>On July 06, 2003 at 00:25:49, Uri Blass wrote:
>>><snipped>
>>>>>Maybe using it for the evaluation is not the most efficient use of a neural
>>>>>network in a chess program. It seems that the way human players manage to search
>>>>>the tree is vastly underestimated.
>>>>>
>>>>>
>>>>>
>>>>>    Christophe
>>>>
>>>>I agree with you that search is underestimated in chess but I also believe
>>>>that search and evaluation are connected because a lot of search decisions are
>>>>based on evaluation of positions that are not leaf positions so you cannot
>>>>seperate them and say search improvement gives x elo and evaluation improvement
>>>>gives y elo.
>>>>
>>>>Uri
>>>
>>>I know that you did not try to seperate between them but my point is that if you
>>>want to do the same as humans in the search then changing the search is not
>>>enough.
>>>
>>>Humans may search position for some seconds and decide that this position is not
>>>good and later search the same position but decide that it is good for them not
>>>because they search deeper but because they learned to change their evaluation
>>>based on searching other lines that leaded to a similiar position.
>>>
>>>Uri
>>
>>
>>
>>Well my point is just that when people talk about an application of ANN in chess
>>they always talk about implementing the evaluation with an ANN, or tuning the
>>evaluation with them.
>>
>>I think it tends to show that the application of ANN to chess has never been
>>done by a "real" chess programmer. Because evaluation is only a part of a chess
>>program. And maybe not the one that can be improved dramatically, or that needs
>>them in order to be improved. Personally I would not use ANNs in the evaluation
>>first, because I think they would be much more efficient somewhere else.
>>
>>On the other hand, you are right. If one could design an ANN to perform the
>>evaluation, it would be wise to use the same ANN (or an extension of it) to
>>guide the search.
>>
>>
>>
>>    Christophe
>
>I believe that the biggest advantage that can be achieved in evaluation is not
>in changing the initial static evaluation but in learning to change the
>evaluation during the game based on the results of the search.
>
>I also do not believe that what humans know is the target and the target should
>be better than what humans know.
>
>programs found better evaluation than humans in backgammon and program may find
>better search rules than humans in chess not because programs are smarter but
>because programs may do trillions of calculation to learn and humans cannot do
>it.
>
>Uri

This is the same utter nonsense crap that i keep seeing AI people write. Yet on
average they even have less experience than you and keep believing in something
they can never proof to be made. If they would have even *toyed* with ANNs a bit
they will understand more about the impossibilities about it.

Show me a backgammon program with an ANN that beats a 5 turns fullwidth
searching backgammon program :)

Of course show it at a machine that you and i have at home.

The average ANN expert is assuming he has to his availability something doing
10^1000 calculations.

That is the major problem when talking to these guys.

Of course you can optimize an ANN for chess in 10^1000 calculations.

But you will then be beaten by a database of just 10^43.

I am however sure that 99% of all ANN interested will not understand what i
write here above, simply because they do not know the running time of the learn
methods applied. If they would read themselves into that, then less crap would
leave their mouth.

Best regards,
Vincent



This page took 0.02 seconds to execute

Last modified: Thu, 07 Jul 11 08:48:38 -0700

Current Computer Chess Club Forums at Talkchess. This site by Sean Mintz.