Path: utzoo!attcan!utgpu!jarvis.csri.toronto.edu!clyde.concordia.ca!uunet!cs.utexas.edu!wuarchive!udel!princeton!phoenix!harnad
From: harnad@phoenix.Princeton.EDU (Stevan Harnad)
Newsgroups: comp.ai
Subject: Re: Cog Sci Fi  (was: STRONG AND WEAK AI)
Summary: The Symbol Grounding Problem
Message-ID: <12126@phoenix.Princeton.EDU>
Date: 11 Dec 89 19:06:24 GMT
References: <11870@phoenix.Princeton.EDU> <16033@megaron.cs.arizona.edu> <8093@cs.yale.edu>
Organization: Princeton University, NJ
Lines: 63



Tom Blenko blenko-tom@CS.YALE.EDU  of
Yale University Computer Science Dept wrote:

> You appear to be arguing both with the assumption that there simply is
> no escape from this situation, and the related proposal that no escape
> is necessary.  If you wish to argue that there is any such thing as a
> symbol grounding problem, I think you have to address both of these
> views (which I understand to be widely accepted).

Before you can argue about anything connected with the symbol grounding
problem you first have to know what it is (preprint available by email):

            THE SYMBOL GROUNDING PROBLEM

             (Physica D 1990, in press)

                 Stevan Harnad
            Department of Psychology
              Princeton University

ABSTRACT: There has been much discussion recently about the scope and
limits of purely symbolic models of the mind and about the proper role
of connectionism in cognitive modeling. This paper describes the
"symbol grounding problem" for a semantically interpretable symbol
system:  How can its semantic interpretation be made intrinsic to the
symbol system, rather than just parasitic on the meanings in our heads?
How can the meanings of the meaningless symbol tokens, manipulated
solely on the basis of their (arbitrary) shapes, be grounded in
anything but other meaningless symbols? The problem is analogous to
trying to learn Chinese from a Chinese/Chinese dictionary alone.

A candidate solution is sketched: Symbolic representations must be
grounded bottom-up in nonsymbolic representations of two kinds:
(1) iconic representations, which are analogs of the proximal sensory
projections of distal objects and events, and (2) categorical
representations, which are learned and innate feature-detectors that
pick out the invariant features of object and event categories from
their sensory projections. Elementary symbols are the names of these
object and event categories, assigned on the basis of their
(nonsymbolic) categorical representations. Higher-order (3) symbolic
representations, grounded in these elementary symbols, consist of
symbol strings describing category membership relations ("An X is a Y
that is Z").

Connectionism is one natural candidate for the mechanism that learns
the invariant features underlying categorical representations, thereby
connecting names to the proximal projections of the distal objects they
stand for. In this way connectionism can be seen as a complementary
component in a hybrid nonsymbolic/symbolic model of the mind, rather
than a rival to purely symbolic modeling. Such a hybrid model would not
have an autonomous symbolic "module," however; the symbolic functions
would emerge in the form of an intrinsically "dedicated" symbol system
as a consequence of the bottom-up grounding of categories' names in
their sensory representations. Symbol manipulation would be governed
not just by the arbitrary shapes of the symbol tokens, but by the
nonarbitrary shapes of the icons and category invariants in which they
are grounded.
-- 
Stevan Harnad  Department of Psychology  Princeton University
harnad@confidence.princeton.edu       srh@flash.bellcore.com
harnad@elbereth.rutgers.edu    harnad@pucc.bitnet    (609)-921-7771