Computer Chess Club Archives


Search

Terms

Messages

Subject: Re: chess and neural networks

Author: Vincent Diepeveen

Date: 14:38:01 07/06/03

Go up one level in this thread


On July 06, 2003 at 16:21:05, Uri Blass wrote:

>On July 06, 2003 at 15:42:25, Vincent Diepeveen wrote:
>
>>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.
>
>I only say that I believe that it can be done.
>It does not mean that I know how to do 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.
>
>Very easy
>the 5 turns fullwidth searching backgammon program is going to lose on time
>every game.
>
>
>
>>
>>The average ANN expert is assuming he has to his availability something doing
>>10^1000 calculations.
>
>I am not ANN expert and I did not suggest ideas how to do it.
>
>>
>>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.
>
>I did not say that the learning methods that are used in backgammon can work in
>chess and it is possible that people need to invent different learning methods.
>Uri

If there was money to earn by programming a backgammon engine, i am sure some
guys who are good in forward pruning algorithms like Johan de Koning would win
every event there. It's like making a tictactoe program and then claiming that
an ANN is going to work.

As we have a saying here: "In the land of the blind, one eyed is King".

That's why i focus upon chess.

In contradiction to you, i know how to do it with ANNs (just like many others
do), i just don't have 10^1000 system time to actually let the learning
algorithm finish ;)

Any approximation in the meantime will be playing very lousy chess...

Hell, with 10^1000 runs, even TD learning might be correctly finding the right
parameter optimization :)

TD learning is randomly flipping a few parameters each time. It's pretty close
to GA's in that respect.






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.