Author: Eric Baum
Date: 11:47:24 05/20/02
Go up one level in this thread
OK then: (1) How much have computer programs benefitted from additional features? Remove all additional features from the top programs except material/piece-square table, and how many rating points would you lose? I'm guessing less than 100, but do you have another estimate? (2) Are there any programs with significant ability to discover new features, or are essentially all the features programmed in by hand. If you believe there are programs that discover useful new features, how many rating points do you think they have gained? And can you give me some idea of what type of algorithm was used? Also, for comparison, does anybody have a recent estimate of rating point gain per additional ply of search? (3) Also, am I right in thinking that modern programs are still more or less doing alpha-beta with quiessence search, or has there been real progress on context dependent forward pruning, leading to substantial rating points gains? On May 20, 2002 at 12:54:48, Robert Hyatt wrote: >On May 20, 2002 at 08:23:35, Eric Baum wrote: > >>How much do modern programs benefit from >>developments beyond alpha-beta search +quiesence >>search? So, if you did the same depth search, >>same quiesence search, same opening book, >>same endgame tables, but replaced the evaluation >>function with something primitive-- say material >>and not much else-- how many rating points would you >>lose? >> >>My recollection is that one of the Deep Thought thesis >>showed a minimal gain for Deep Thought from >>extensive training of evaluation function-- >>it gained some tens of rating points, but >>less than it would have gained >>from a ply of additional search. Has that changed? > > >You are mixing apples and oranges: > >apples: which evaluation features does your program recognize? > >oranges: what is the _weight_ you assign for each feature you recognize? > >Those are two different things. The deep thought paper addressed only the >oranges issue. They had a reasonable set of features, and they set about >trying to find the optimal value for each feature to produce the best play. > >Adding _new_ evaluation features would be a completely different thing, >of course...
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