> -----Original Message----- > From: Jason House <[EMAIL PROTECTED]> > To: computer-go <computer-go@computer-go.org> > Sent: Thu, 6 Dec 2007 4:44 pm > Subject: Re: [computer-go] Re: evaluating monte carlo results
> On Dec 6, 2007 4:22 PM, <[EMAIL PROTECTED]> wrote: > > > > My program is riddled with code to try and make use of this. (It's always > > bothered me that UCT relies on the ?> > standard deviation of (often) multi-modal distributions.) It hasn't made my engine any stronger but it has helped? > > me understand some things better. > Care to elaborate on what you've come to learn?? I too have discomfort with > some of the ways UCT works. If I start from an empty board and make a histogram of the final scores of random playouts, it tells me a bit about my playout algorithm. Smarter playout algorithms lead to lower (IIRC) required komis. >From a root node, looking at the tails of the histogram of final scores tells >me something about how much can potentially be gained by applying the mercy >rule and where to set the threshold. It seems reasonable to suppose that for a root node where the distribution of outcomes is changing rapidly (the peaks moving around) or has widely separated peaks, maybe it should still be exploring more, or maybe it just needs more time, this turn, to settle. I personally haven't gotten any traction here but wouldn't be too surprised if someone else has more luck. It gets into issues of whether any margin of victory is very significant. I notice the "why don't we make UCT just a little greedier" discussion has cropped up yet again. I've tried that both ways, as have many others here. ________________________________________________________________________ More new features than ever. Check out the new AIM(R) Mail ! - http://o.aolcdn.com/cdn.webmail.aol.com/mailtour/aol/en-us/text.htm?ncid=aimcmp00050000000001
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