Author: Dann Corbit
Date: 20:55:51 12/14/99
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On December 14, 1999 at 23:38:55, Christophe Theron wrote: [snip] >Both subjects are related in my opinion. Here is what I think. It is not the >word of god of course, but I have this in mind when I work and so far it works >rather well for me: > >* I think that a good balanced program will be good at any time control > >* I think that playing with long time controls gives results much closer to 50% >than playing with short time controls > >These points mean that (amongst other things): > >* You must also pay attention to the results your program gets at fast time >controls. I have heard many people saying that blitz games mean nothing, but if >a program does much better at long time controls than in blitz, in my opinion it >is not a balanced program, and it has a structural weakness somewhere > >* At long time controls, "outside" effects will have a bigger impact on the >results and must be worked out carefully: book and learning come to my mind. > >I have nothing to back up these points, except that so far it has been my >philosophy and at least it is not worse than other points of view. At least it >stands the test of real life... If the algorithms employed are very similar, the above will be mostly true. But there are some exceptions. 0. If the fundamental algorithm employed by program A has a better O(f(n)) behavior than program B, at *some* point program A will overtake program B. For instance, if I have a hash table implemented as a linked list (stupid, I know) as long as there are only 100 entries in both tables, there won't be a noticeable difference. But if there are one hundred thousand it will be noticeable and if there are twenty million it will dominate. 1. Similarly, a program may have some clever extensions that don't even kick in until we get to ten ply. So at short time controls, they don't even happen. But at long time controls they take effect. 2. The effect of time might even be parabolic. Take the example of sorting by multiple list insertion. Shell-sort will clobber it at tiny set size. Then it is fast as greased lightning. But as set size increases, the extra memory and memory management sets cause it to be overtaken by counting sort variants. So algorithms are optimal for a certain domain. The biggest reason that programs level out at long time controls is the exponential nature of chess. If program A can get to ply 9 in 8 seconds and ply 9 in 16 seconds and ply 10 in 32 seconds... at some point the advancement to the next ply will be a really long interval. So some inferior program may catch up as far as ply completion because of the nature of the time control used. E.g. program A gets to ply 15 in 1024 seconds (17 minutes) but takes 34 minutes to get to ply 16. In the meantime, program B gets to ply 15 in 30 minutes and the time for the move is up at 1/2 hour per ply, just before A finishes ply 16. As you can see, the "slop" gets bigger and bigger. Eventually, both programs will virtually hang -- accomplishing nothing.
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