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Subject: Re: Knowledge again, but what is it?

Author: Jay Scott

Date: 14:57:12 02/25/98

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On February 25, 1998 at 15:19:50, Don Dailey wrote:

>But now a good question is: What does  the probability measure?  Is it
>the  probability that  the computer   will win?   I think the  correct
>interpretation should be  "the probability that the  position is a won
>position."  A dead draw should  be considered as 50% of  a win, or 50%
>probability  of  winning.  If we   say  it's the probability that  the
>computer will win, then it's  completely ambiguous, because we do  not
>know what assumptions to make about the strength of  the opponent!  If
>Cilkchess is down half a pawn against most humans then it's chances of
>winning are still  greater than 50%.  Another possible  interpretation
>is the probability of winning against an equal opponent (whatever that
>is!)

A crucial question, in my view! What your evaluation probability
"really" measures depends on how you constructed the evaluation.
Usually the game-theoretic values of positions aren't available,
so you have no way to construct an evaluator which measures the
true probability that a position is a win given what the program
knows about it. That still makes sense as an ideal to strive for,
though.

If you construct the evaluator from self-play, then the probability
will be the program's probability of winning against itself
from that position. If you construct it from games against a
variety of opponents, like KnightCap, then it's the probability
of winning against some notion of the average opponent.

I'm not happy with either of those possibilities. I would like
the probability to be more meaningful. I would like some kind
of opponent model, fancy or crude, so that I could say that it
was the probability of winning this game, against this opponent.
A crude model might contain no more information than the opponent's
rating, which may or may not be useful information to a real
program. A fancy model could take into account whether the
opponent is a human or a program (crafty does this) and any
other helpful info you can think up. If you don't know anything
about the opponent then you have to fall back on an empty
opponent model anyway, which is equivalent to having no special
opponent model, so this isn't too big a deal.

Another point, to repeat something I've said before, is that
you don't really want to measure the probability of winning
from a position; you want to measure the utility of reaching a
position, which depends on the probability of winning and the
probability of drawing. Counting a draw as half a win is not
always right; it doesn't distinguish between positions which
are dead drawn and positions which are dynamically equal, and
sometimes you care.

  Jay



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