Author: Dann Corbit
Date: 17:51:22 12/28/05
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On December 28, 2005 at 19:56:16, David Dahlem wrote: ><snip> > >>The idea works like this: >>Suppose that I evaluate a pawn as 100.0 centipawns. How much should a knight be >>worth? Probably somewhere between 200 and 500 centipawns. Now, if I choose a >>bad value (like 200 centipawns) then I should solve less problems. If I choose >>a good value (probably fairly close to 300), then I will solve more problems. >>So I iterate over 12,000 quiet chess positions with known solutions and see what >>value of the parameter gives me the most solutions. >> >>Now, the value found will NOT be optimal for play. But it will be a good >>starting point for experimentation. And I will also have a pretty good idea >>about what sort of range will produce reasonable results. >> > >Thanks Dann. I'm not really a fan of tuning with test suites. I just build >several test versions with different bonus values, and run a lot of test games. >It's time consuming, but i have more confidence in the results. :-) I agree that test suites are not good for game play. But they are useful to get an idea of the general ballpark for the right value of a parameter. >>So... >> >>Big_again: >>Tell the program that a knight is worth 'start' (e.g. 200) centipawns. >>Have it try to solve 12000 positions at depth k. >>Count the solutions. >> >>Little_again: >>Tell the program that a knight is worth Current Knight value + Increment >>centipawns. >>Have it try to solve 12000 positions at depth k. >>Count the solutions. >> >>If current knight value is bigger than 'limit' (e.g 500) goto Little_again >> >>Fit the curve and write the solution. (We are interested in the apex of a >>parabola that has only a maximum and not a minimum.) >> >>If depth is not at maximum desired depth, increment depth 'k' and goto Big_again
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