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From: hamscher@HT.AI.MIT.EDU (Walter Hamscher)
Newsgroups: comp.ai.digest
Subject: The Success of AI
Message-ID: <8710191427.AA26387@ht.ai.mit.edu>
Date: Mon, 19-Oct-87 10:27:22 EDT
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Posted: Mon Oct 19 10:27:22 1987
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   Date: 17 Oct 87 22:09:05 GMT
   From: cbmvax!snark!eric@rutgers.edu  (Eric S. Raymond)

	* * *

   I never heard of this line of research being followed up by anyone but
   Doug Lenat himself, and I've never been able to figure out why. He later
   wrote a program called EURISKO that (among other things) won that year's
   Trillion-Credit Squadron tournament (this is a space wargame related to
   the _Traveller_ role-playing game) and designed an ingenious fundamental
   component for VLSI logic. I think all this was in '82.

See Lenat & J.S. Brown in AI Journal volume 23 #3, 1984: "Why AM
and EURISKO Appear to Work".  The punchline of the article
(briefly) is that AM seems to have succeeded in elementary set
theory because its own representation structures (i.e., lists),
were particularly well suited to reasoning about sets.  It
started breaking down at exactly the places where its
representation was inadequate for the concepts.  For example,
there was no obvious way to move from its representation of the
number n as a list of length n, to a positional representation
that would make it more likely to discover things like
logarithms.  Furthermore, its operations on procedures involved
local modifications to procedures expressed as list structures,
and as long as the procedures were compact these "mutations"
were likely to produce interesting new behavior, but as the
procedures get more complex, arbitrary random local
modifications had a vanishingly low success ratio.  Hence it
would seem that direction to go from this insight is to make
programs that can learn new representations.  There are probably
not enough people working on that.  But anyway this is getting
off the subject, which is whether AI has had any successes.
Whether you want to count AM as a success is half-empty /
half-full issue; the field surely learned something from it, but
it surely hasn't learned esuRockw