tech report available

[Dave.Touretzky@B.GP.CS.CMU.EDU]

The following tech report is now available:

BoltzCONS:  Dynamic Symbol Structures in a Connectionist Network

		    David S. Touretzky

		 School of Computer Science
		 Carnegie Mellon University
		 Pittsburgh, PA  15213-3890

		      CMU-CS-89-182

 BoltzCONS is a connectionist model that dynamically creates and
manipulates composite symbol structures.  These structures are implemented
using a functional analog of linked lists, but BoltzCONS employs
distributed representations and associative retrieval in place of a
conventional memory organization.  Associative retrieval leads to some
interesting properties.  For example, the model can instantaneously access
any uniquely-named internal node of a tree.  But the point of the work is
not to reimplement linked lists in some peculiar new way; it is to show how
neural networks can exhibit compositionality and distal access (the ability
to reference a complex structure via an abbreviated tag), two properties
that distinguish symbol processing from lower-level cognitive functions
such as pattern recognition.  Unlike certain other neural net models,
BoltzCONS represents objects as a collection of superimposed activity
patterns rather than as a set of weights.  It can therefore create new
structured objects dynamically, without reliance on iterative training
procedures, without rehearsal of previously-learned patterns, and without
resorting to grandmother cells.

This paper will appear in a special issue of Artificial Intelligence
devoted to connectionist symbol processing.

Note: the BoltzCONS work first appeared in print in 1986.  This paper
offers a more detailed description of the model than previously available,
and a much more thorough analysis of its significance and weak points.

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