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 interesting. 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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