Computer Chess Club Archives




Subject: Re: chess and neural networks

Author: Vincent Diepeveen

Date: 09:44:12 07/02/03

Go up one level in this thread

On July 01, 2003 at 21:57:14, Dann Corbit wrote:

>On July 01, 2003 at 18:28:19, Djordje Vidanovic wrote:
>>On July 01, 2003 at 15:35:22, Charles Roberson wrote:
>>>  KnightCapp uses neural networks and I beleive some papers were published
>>>  about it.
>>>  ICCA Journal vol 14 September 1991 has a paper "Neural Networks as a Guide
>>>  to Optimization" by A. van Tiggelen. It discusses automated tuning of a
>>>  position evaluator.
>>>   Charles
>>KnightCap is pretty good at learning both opening lines and evals. I finally
>>managed to compile it to run well on my Linux box and I started it without any
>>brain.dat actually and with default coeffs.dat.  In a little while it started
>>beating Gnuchess (say after about 400 blitz games) and even managed to win
>>several games against Crafty.  It IS getting stronger, but of course its limits
>>are close   because the search parameters have to be better. Although some
>>programmers do think that its qsearch is fine...
>>I find KnightCap a lot of fun, especially its "mulling" over the losing lines
>>and finding out better lines while idle.  I find it an extremely tough opponent,
>>as tough, if not tougher than some well known programs.  After about 500 blitz
>>games it started beating Scidlet as well.  Scidlet is considered to be about
>>2200, so I'd venture to say that KnightCap progressed from a 1900 player to a
>>2250 player in about 500 games.  There are a couple of papers on KnightCap in
>>pdf format, by Andrew Tridgell and Jonathan Baxter.  And, of course, the famous
>>paper on Temporal Difference learning by Rich Sutton that started it all.  All
>>can be found on the Net.
>>Neural learning seems to be quite succesful in games that require fewer
>>parameters than chess, such as backgammon (JellyFish, GnuBackgamon, BGBlitz) or
>>checkers.  Chess is tough as it has lots of parameters that matter.
>I think KnightCap is the most successful neural-net type of program.  Lots of
>programs have some sort of learning, but neural nets are not used successfully
>very often.
>I think to work really well one would need:
>1.  A very fast computer
>2.  A very large number of evaluation terms
>3.  A very long time slot after a loss so that the calculations could be
>absorbed into the eval.
>I know some other people working on neural nets, and I think the results are
>The early attempts (e.g. Sal) were unfathomably lame.  But then again, so are
>the early attempts at anything and everything.
>The question is:
>Can a neural net make a program play better?
>I think the answer is pretty clearly "Yes!"

I agree with you under the condition that you give it 10^120 computing time to
optimize for chess.

The problem is that todays search algorithms + evaluations are needing less to
solve chess than a NN needs to just optimize for a few parameters.

>The more salient question is:
>Is there a more effective way to make a program play better?
>I don't think that question has been answered or even can be answered.
>That's just one of the things that makes chess programming so interesting.

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