Author: Dan Homan
Date: 15:58:09 06/11/99
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On June 11, 1999 at 17:43:59, Jon Dart wrote: >On June 11, 1999 at 10:39:13, Dan Homan wrote: > > >>I've been thinking about this alot recently. One thing that occurred >>to me is that this might be an effecient way to create a neural net based >>program. >> >>Say the neural net (nn) is responsible for filling a piece-square table... >>Then the nn could operate either once, at the root of the search, or >>only selected times during the search. This would largely overcome >>the major drawback of nn evaluations which is that they are slow. >> >>Then we could have a TD based learning program that also learns >>evaluation features! >> >>I've thought alot about building my next chess program on this >>idea.... The one major drawback that I see is that there will be >>a relatively large number of parameters for learning to adjust. >>The other drawback is that I really know nothing useful about >>programming neural nets. :) >> >> - Dan > >KnightCap (http://samba.anu.edu.au/KnightCap/) has learning of >its evaluation parameters (as well as book learning) .. it >appears to work very well. But I don't believe it does non-leaf >evals. I've read the knightcap papers. I was thinking it might be interesting to go a step beyond learning values for pre-defined evaluations features and actually learn new features not previously defined. Neural Nets will allow this, but they are slow.... so I was thinking of using a neural net to fill piece-square tables at the start of the search or at well-defined points in the search. Maybe this wouldn't work, but it is interesting to think about. - Dan > >--Jon
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