Author: Sune Fischer
Date: 07:37:19 02/06/02
Go up one level in this thread
On February 06, 2002 at 10:19:29, William H Rogers wrote: >So it would seem, but the search is exponential and not linear. >I think you should not consider the "depth" but rather the number of nodes >searched. >If you go one ply deeper then (assuming your branch factor (BF) is not too depth >dependent) you a factor of BF more nodes, this ratio is fairly constant so I'd >go with Uri's definition. > >The diminishing returns issue is probably an effect of converging towards the >ideal move as often as possible. > >-S. > >I vote for your analisis. Just for an example lets say that a program can only >search to a level of 10 plys and it thinks that it has found its very best move, >then lets assume that we can search 2 to 4 plys deeper and it discovers that >there is a better move that can help it win the game. This happens all of the >time in chess and in other zero-sum games. The deeper you search the better you >game will be, of course it really depends on your evaluation routine is >basically sound in the first place. >Bill I agree, if we forget about chess programs and just study chess, then we can ask: A:) What is the percentage of ideal moves that can be found at ply 0 ? B:) What is the percentage of ideal moves that can be found at ply 1 ? C:) What is the percentage of ideal moves that can be found at ply 2 ? D:) What is the percentage of ideal moves that can be found at ply 3 ? E:) What is the percentage of ideal moves that can be found at ply 4 ? etc... Now obviously this percentage cannot be constant since it must sum to 1, so it has to be descending which means diminshing returns. Exactly what type of function that is I do not know, but it would interesseting to find out :) -S.
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