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From: SOWA@IBM.COM (john Sowa)
Newsgroups: comp.ai.digest
Subject: Knowledge Soup
Message-ID: <102487.200341.sowa@ibm.com>
Date: Mon, 26-Oct-87 02:21:03 EST
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Posted: Mon Oct 26 02:21:03 1987
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An abstract of a recent talk I gave found its way to the AIList, V5 #241.
But along the way, the first five sentences were lost.  Those sentences
made a distinction that was at least as important as the rest of the
abstract:

   Much of the knowledge in people's heads is inconsistent.  Some of it
   may be represented in symbolic or propositional form, but a lot of it
   or perhaps even most of it is stored in image-like forms.  And some
   knowledge is stored in vague "gut feel" or intuitive forms that are
   almost never verbalized.  The term "knowledge base" sounds too precise
   and organized to reflect the enormous complexity of what people have
   in their heads.  A better term is "knowledge soup."

Whoever truncated the abstract also changed the title "Crystallizing
theories out of Knowledge Soup" by adding "(knowledge base)".  That
parenthetical addition blurred the distinction between the informal,
disorganized knowledge in the head and the formalized knowledge bases
that are required by AI systems.  Some of the most active research in
AI today is directed towards handling that soup and managing it within
the confines of digital systems:  fuzzy logic, various forms of default
and nonmonotonic reasoning, truth maintenance systems, connectionism
and various statistical approaches, and Hewitt's due-process reasoning
between competing agents with different points of view.

Winograd and Flores' flight into phenomenology and hermeneutics is based
on a recognition of the complexity of the knowledge soup.  But instead of
looking for ways of dealing with it in AI terms, they gave up.  Although
I sympathize with their suggestion that we use computers to help people
communicate better with each other, I believe that variations of current
AI techniques can support semi-automated tools for knowledge acquisition
from the soup.  More invention may be needed for fully automated systems
that can extract theories without human guidance.  But there is no clear
evidence that the task is impossible.