CADE-14 workshop CFP

[maire@fit.qut.edu.au]

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                   FIRST CALL FOR PAPERS

                     CADE-14 WORKSHOP 
            July 13, 1997, Townsville, Australia

            --------------------------------------------
             CONNECTIONIST SYSTEMS FOR 
  KNOWLEDGE REPRESENTATION AND DEDUCTION
            --------------------------------------------

     Joachim Diederich,  Frederic Maire  &  Ross Hayward

               Neurocomputing Research Centre
            Queensland University of Technology
            Brisbane 4001 Queensland, Australia
                   Phone: +61 7 3864-2143
                   Fax:   +61 7 3864-1801
      E-mail: {joachim,maire,hayward}@fit.qut.edu.au


                           GOALS

The objective of the workshop is  to  provide  a  discussion
platform for researchers  interested in  Artificial Intelli-
gence (AI), Neural Networks (NN),   Automated Reasoning  and
Deduction.   The workshop should be of considerable interest
to computer scientists, mathematicians and engineers as well
as   to  cognitive scientists  and  people interested in  NN
applications which try to bridge the gap between symbolic AI
systems and connectionist networks.


                        INTRODUCTION

Connectionist  systems  are  attractive  because  they  have
highly  desirable properties such as fine-grain parallelism,
fault tolerance and automatic learning.  For  a  long  time,
they   lagged  behind  symbolic  AI  systems  for  knowledge
representation and automated reasoning.  But over  the  last
ten years,  several  connectionist  knowledge representation
systems have  been  introduced  with  greater expressive and
inferential power than  previous systems  (e.g. Pinkas 1991,
Shastri & Ajjanagadde 1993,  Lange & Dyer 1989,  Diederich &
Kurfess 1994, Derthick, 1988).

                        SIGNIFICANCE

The  rapid  and  successful  proliferation  of  applications
incorporating Artificial Neural Network methods and  systems
in fields as  diverse as commerce,  science,  industry   and
medicine,  offers a clear testament to the capability of the
NN paradigm.  However, NNs  are generally weak  methods  for
knowledge  representation.  In contrast to symbolic systems,
neural networks  have  no  explicit,  declarative  knowledge
representation  and therefore have considerable difficulties
in  generating  complex   or   embedded   (e.g.   recursive)
structures.  In  neural  networks,  knowledge  is encoded in
numeric parameters (weights) and generally distributed.  For
NNs  to gain an even wider degree of  user acceptance and to
enhance their overall utility as learning and generalisation
tools, it is highly desirable (if not essential) to overcome
their limitations as representational systems.


        DISCUSSION POINTS FOR WORKSHOP PARTICIPANTS

 1. Oscillatory or signature passing models such as SHRUTI
 (Shastri & Ajjanagadde,  1993)  or  ROBIN  (Lange  &  Dyer,
1989).

 2. Systems based on  energy  minimisation  such  as  Pinkas
(1991a,b) or Derthick (1988).

 3.  Integrated  modular  systems  that  employ  multi-layer
feedforward networks and simple recurrent networks (e.g.
Diederich & Kurfess, 1994). Learning and representation need
to interact here and the representational expressiveness
needs to be improved.

 4.  Logical  formalism   representable   in   connectionist
networks

 5. Representing  reasoning  processes  in  a  connectionist
architecture

 6. Relevance of the connectionist approach to overcome  the
main obstacles to  the  automation   of   reasoning   (clause 
retention, inadequate  focus, redundant information, clause 
generation, demodulation, metarules etc.)

 7. Learning for Connectionist Representation Systems

 8. Learning direction strategy to reduce  the  severity  of
the obstacle of inadequate focus.


SUBMISSION OF WORKSHOP EXTENDED ABSTRACTS/PAPERS

Authors are invited to submit 3 copies of either an extended abstract  or
full paper relating to one of the topic areas listed above. Papers should
be written in English in single column format and should be limited to no
more than eight, (8) sides  of A4 paper including figures and references.
 We encourage e-mail submissions in Postscript.

Please include the following information in an accompanying cover letter: 
Full title of paper, presenting author's name, address, and telephone and
fax numbers, authors e-mail address.

Submission Deadline is April 21,1997  with  notification to authors by
May 5, 1997 and final postscript versions for the proceedings due by
June 2, 1997.


For further information,  inquiries,  and paper  submissions 
please contact:

Joachim Diederich,  Frederic Maire  & Ross Hayward
               Neurocomputing Research Centre
            Queensland University of Technology
            Brisbane 4001 Queensland, Australia
                   Phone: +61 7 3864-2143
                   Fax:   +61 7 3864-1801
      E-mail: {joachim,maire,hayward}@fit.qut.edu.au



More information about the CADE-14  workshop  series is available from:

   WWW:  http://www.cs.jcu.edu.au/~cade-14/

Information about Workshop participation fees are available from:

   WWW: http://www.cs.jcu.edu.au/~cade-14/CADE-14/RegoForm.html