Connectionism & Reasoning: BBS Call for Commentators
Stevan Harnad
harnad at Princeton.EDU
Tue Jun 9 21:28:24 EDT 1992
Below is the abstract of a forthcoming target article on connectionism
and reasoning by Shastri & Ajjanagadde. It has been accepted for
publication in Behavioral and Brain Sciences (BBS), an international,
interdisciplinary journal that provides Open Peer Commentary on
important and controversial current research in the biobehavioral and
cognitive sciences. Commentators must be current BBS Associates or
nominated by a current BBS Associate. To be considered as a commentator
on this article, to suggest other appropriate commentators, or for
information about how to become a BBS Associate, please send email to:
harnad at clarity.princeton.edu or harnad at pucc.bitnet or write to:
BBS, 20 Nassau Street, #240, Princeton NJ 08542 [tel: 609-921-7771]
To help us put together a balanced list of commentators, please give some
indication of the aspects of the topic on which you would bring your
areas of expertise to bear if you were selected as a commentator. An
electronic draft of the full text is available for inspection by anonymous
ftp according to the instructions that follow after the abstract.
____________________________________________________________________
FROM SIMPLE ASSOCIATIONS TO SYSTEMATIC REASONING:
A Connectionist representation of rules, variables, and dynamic
bindings using temporal synchrony
Lokendra Shastri
Computer and Information Science Department
University of Pennsylvania
Philadelphia, PA 19104
shastri at central.cis.upenn.edu
Venkat Ajjanagadde
Wilhelm-Schickard-Institut
University of Teubingen
Sand 14 W-7400 Tuebingen, Germany
nnsaj01 at mailserv.zdv.uni-tuebingen.de
KEYWORDS: knowledge representation; reasoning; connectionism;
dynamic bindings; temporal synchrony, neural oscillations, short-
term memory; long-term memory; working memory; systematicity.
ABSTRACT: Human agents draw a variety of inferences effortlessly,
spontaneously, and with remarkable efficiency --- as though these
inferences were a reflex response of their cognitive apparatus.
Furthermore, these inferences are drawn with reference to a large body
of background knowledge. This remarkable human ability is hard to
explain given findings on the complexity of reasoning reported by
researchers in artificial intelligence. It also poses a challenge for
cognitive science and computational neuroscience: How can a system of
simple and slow neuron-like elements represent a large body of
systematic knowledge and perform a range of inferences with such speed?
We describe a computational model that takes a step toward addressing
the cognitive science challenge and resolving the artificial
intelligence puzzle. We show how a connectionist network can encode
millions of facts and rules involving n-ary predicates and variables
and can perform a class of inferences in a few hundred milliseconds.
Efficient reasoning requires the rapid representation and propagation
of dynamic bindings. Our model achieves this by representing (1)
dynamic bindings as the synchronous firing of appropriate nodes, (2)
rules as interconnection patterns that direct the propagation of
rhythmic activity, and (3) long-term facts as temporal pattern-matching
sub-networks. The model is consistent with recent neurophysiological
findings which suggest that synchronous activity occurs in the brain
and may play a representational role in neural information processing.
The model also makes specific, psychologically significant predictions
about the nature of reflexive reasoning. It identifies constraints on
the form of rules that may participate in such reasoning and relates
the capacity of the working memory underlying reflexive reasoning to
biological parameters such as the frequency at which nodes can sustain
oscillations and the coarseness of synchronization.
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To help you decide whether you would be an appropriate commentator for
this article, an electronic draft is retrievable by anonymous ftp from
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bbs.shastri). Please do not prepare a commentary on this draft. Just
let us know, after having inspected it, what relevant expertise you
feel you would bring to bear on what aspect of the article.
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---
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