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Subject: Re: Rating Points and Evaluation Function

Author: Eric Baum

Date: 12:41:47 05/20/02

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On May 20, 2002 at 15:05:15, Russell Reagan wrote:

>I would bet that a program with an evaluation function that primitive wouldn't
>break the 2000 elo mark, or maybe between 2000-2100 at best. Of course I could
>be wrong and someone like Bob or Dan Corbit could tell you better the
>differences between a program with a simple evaluation function vs. a program
>with a complex one,

Hopefully one of the experts will respond :^)

>There are programs like this, and they do learn new features. They generally use
>neural nets or something like that. They also generally stop improving at about
>a beginner level.

I'm only interested in ones that actually do better...

>
>>Also, for comparison, does anybody have a recent estimate of rating
>>point gain per additional ply of search?
>
>I don't, but someone does I'm sure. I would guess however that at some point you
>aren't going to get many more rating points, and then once you reach a really
>deep depth, you will start to see more jumping up of the rating, then another
>diminishing returns area, then another jump, and so on, until you reach a ply
>depth where you solve the game.

Hsu used to give talks graphing rating increase vs ply and claimed something
like a 200 point increase per ply (I'm just going from memory, so number might
be in error). He used to claim, I think, that this increase would continue
to arbitrary depth. (Of course, he was trying to convince IBM management to
continue funding development of his parallel machine, but subsequent events
lent some credence to his previous prognostication.)


>And since I don't even know what "context dependent forward pruning" is, maybe
>you could explain that :)

What I mean is some method of doing something like what humans do:
deciding to pursue some lines of search and abandon others, based on the
board position. Logistello, for example, does additional pruning beyond
alpha-beta in Othello, and the Deep Blue crowd tried singular extensions,
but again the singular extensions never added much to their ratings either.
These were forward pruning methods, but didn't really look at the board
position, really only looked at the evaluation of the board and evaluation of
other lines
to make a decision what to prune. So singular extensions prunes when one line
is much more highly evaluated than others, independent of what the actual
positions are, but my understanding is SI didn't lead to much gain. Is there any
serious forward pruning going
on, leading to real ratings improvements? And if so, is anybody gaining
from pruning based on position as opposed to merely numerical evaluation?








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