Computer Chess Club Archives




Subject: Re: chess and neural networks

Author: Dann Corbit

Date: 18:57:14 07/01/03

Go up one level in this thread

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!"

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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