Author: David Dahlem
Date: 16:56:16 12/28/05
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<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. :-) Regards Dave >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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