Author: Djordje Vidanovic
Date: 15:28:19 07/01/03
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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. Djordje
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