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Subject: Re: LCT II Fin4, Deep Thought, and Deep Blue (was Re: LCT II results...)

Author: Albert Silver

Date: 14:53:07 01/05/98

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On January 05, 1998 at 12:56:49, Bruce Moreland wrote:

>Hold on, I would like to not turn this into another "Deep Blue sucks"
>thread.
>
>I don't think a lot of knowledge is necessary to solve this.  I turned
>off my program's endgame databases, and let it search this, and it found
>h3 pretty quickly, and returned a draw score for this move in about 16
>seconds on a P2/300 (ply 12).
>
>This is not a big knowledge problem.
>
>bruce

Deep Blue sucks??? That isn't exactly what I said, and if that's the
impression I gave, then I'd like to clarify. You say you turned off you
endgame databases. Fine, but what does that have to do with knowledge?
Disable Ferret's knowledge of the game (or at least most of it) and then
run it through. How many plies until 59...h5 is clearly and materially
lost? Not evaluated lost, but materially lost. Why do some programs find
the solutions to problems faster than others? Is it just because they
search deeply quicker than others? If that was the case, then a program
like Hiarcs should underperform in test suites (for example) compared to
power searchers in every test, every single time. That's not the case
obviously. Take the example of Junior's game against Comet in which it
played the losing 47...Rxd3. I fed the position after 47.Kg1 to Fritz 5
and watched to see how deeply it had to go to see that Rxd3 is LOSING
(not just when it would choose another move). After 13 plies it
announces that Rxd3 draws, and after 14 plies it sees that this loses
outright. I then let Hiarcs 6 look at it, and after 6 plies it says that
Rxd3 draws and after 7 plies it says that it loses. I'll admit this
example is unusually extreme, and that there are opposite examples of
Fritz's searching showing results far faster than Hiarcs. While this
clearly argues in favor of knowledge based programs, I also don't
believe that every knowledge ladden will reach the solution at this same
depth (some may do it at a shallower depth, and some may require an
extra ply or so). The difference would then be not just a matter of
knowledge, but also in how this knowledge was administrated (or
prioritized). My argument wasn't actually about Deep Blue and Deep
Thought, though I used them for the sake of examples, but on how
knowledge is used in programs and how it is prioritzed in the evaluation
function. If a program is juggling 300 elements in a position but
doesn't know which ones to prioritize, then it's knowledge is useless,
and might even be a hindrance. But to say that Deep Blue sucks?.... I
couldn't disrespect Kasparov that much. :-)

                                 Albert



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