Author: Robert Hyatt
Date: 16:41:57 07/03/03
Go up one level in this thread
On July 02, 2003 at 12:35:38, Vincent Diepeveen wrote: >On July 01, 2003 at 13:32:19, Ralph Stoesser wrote: > >>Hello *, >> >>Why no top engine uses neural networks for positional evaluation in non-tactical >>situations? Are there interesting publications about neural networks and chess >>programming? >> >>Ralph > >because > a) NN are too slow 20 years ago chess programmers were saying this about _all_ high-level languages, as they wrote in assembly. > b) they do not work very well for situations they are not trained for > and in chess you always explore new positions which are not trained yet, > which is an easy thing to understand once you understand that chess has > 10^44 positions and you could train perhaps for 10^2 positions at > most very well so missing around 10^40 somewhere. That's a training issue. It isn't unsolvable. > c) the persons that say they work for similar situations are on drugs I can show you an aircraft tracking ANN that worked just fine. Very complex problem tracking multiple targets from one radar image to the next. > d) training for chess takes more time than solving chess brute force costs > In fact my approximation is 10^120 to train for chess a NN, under > the condition that the NN has all the relevant knowledge. That is quite > a big problem when you consider chess is x.10^43 according to > latest findings. Then humans can't play chess either. Because we have the same sort of NN training problem. bottom line is that exhaustive training either isn't required, or else humans can't play chess. One or the other _must_ be true. And since humans do play the game well...
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