Author: Sune Fischer
Date: 02:07:19 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 >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. I'm not sure nets are really that good for chess. First of all you would need a huge NN with the capacity to remember of all these patterns, I mean thousands probably tens of thousands of neurons and weights. This would make it very slow to compute at runtime, if not using special designed hardware. Secondly, even if one could design a good cost function, like I suppose in KnightCap, what would the network learn? Well, it might learn double pawns tend to be bad, it might learn 2 rooks is better than 1. We already know this of course, and I think it's going to be hard to train the network to do better than handtuning here. The only interesting aspect would be, if the NN could teach itself some rules we where not aware existed. Honestly I don't think that is very likely, what is far more likely is that the net learns something that is wrong. When it's working for humans, I think it's because we litteraly do have billions of neurons, and it's not that slow because it's running in parallel. In spite of this our evaluation is nowhere near perfect, we aren't all world champions and we still need to search for tactics. -S.
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