[computer-go] Scoring - step function or sigmoid function?

[Stefan Kaitschick]

It seems to be surprisingly difficult to outperform the step function  when 
it comes to mc scoring.
I know that many surprises await the mc adventurer, but completely 
discarding the final margin of victory just can't be optimal. The sigmoid 
function can be tinkered with ofcourse, by making its slopes steeper and/or 
by awarding bonus points for victory. But if it looks like the step function 
in the end, then computational resources could have been saved by just using 
the step function from the start. The power of the step function is that it 
directly awards what we are really interested in - victory. And an mc 
program, holding on to a half point victory in the endgame,  is a thing of 
beauty and terror. But in the opening, where the scoring leaves are 300 
moves away from the root, surely a putative half point win doesn't translate 
to a significant advantage, where as a 100 point win would. My suggestion is 
this: how about backing up the individual outcomes through the  tree and 
then do the evaluation at the intermediate nodes, using the sigmoid function 
and the parameters depending on the distance from the root? This might be 
too expensive computationaly, but shortcuts could be devised. For example, 
wins could be sorted into a couple of different categories( from half point 
win to landslide), and those categories could be evaluated differently, 
depending on the distance to the root.

Stefan 

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