Learning Control at SCI'97

[Karl Mathia]

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                           LAST Call for Papers:

                   SPECIAL SESSION ON LEARNING CONTROL
                   -----------------------------------

                                at the

                        WORLD MULTICONFERENCE ON
             SYSTEMICS, CYBERNETICS AND INFORMATICS (SCI'97)
                            Caracas, Venezuela
                              July 7-11, 1997

           (Extended Deadline for Abstracts: March 31, 1997)

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LEARNING CONTROL
----------------
"Learning Control" is a term attributed to a broad class of self-tuning
processes,  where the performance of the controlled system with respect
to a particular task is self-improved based on the performance for
previous identical tasks.  The idea of self-learning control systems is
aesthetically appealing and represents a fundamental step towards fully
autonomous systems. This is of advantage when dealing with uncertain or
changing systems.

The major difference between adaptive and learning control is sometimes
characterized in terms of 'local' and 'global' learning.  An adaptive
systems continuously adapts to changes in environment and system para-
meters (local),  whereas a learning systems memorizes and recognizes
previously experienced situations (global).  The classification of
learning control will be one of many topics at this SCI'97 session. We
invite you to present your recent research results, learn about current
avenues in the field, and to meet interesting people.

We look for quality papers which cover the topics outlined below.
Similar work is also welcome.


OVERVIEW OF SCI'97
------------------
SCI'97 is a truly multi-disciplinary conference, covering intelligent
computing, information theory,  cybernetics,  social and biological
systems, psychology, and applications.  General information about the
conference is listed below or can be found at the website

  http://www.iiis.org/

SCI'97 is an ideal platform for a special session on "Learning Control",
an emerging discipline which is receiving more and more attention from
both the academic and industrial controls community, due to the increasing
complexity of (technical) systems.


TOPICS
------
The Learning Control Session will include, but is not limited, to the
following topics (further suggestions are encouraged):

     * Classification of learning control systems.

     * Mathematical learning theory in a controls context.

     * Biological or social self-learning control mechanisms and
       their extension to technical systems.

     * Human operator modeling.
       Human operators are (currently) the ultimate learning controller
       for complex systems.

     * Neurocontrol, using biological or artificial neural networks.

     * Fuzzy logic and learning.

     * Optimal Control type learning algorithms (adaptive critics,
       Q-learning, etc.).

     * Variable structure learning of controllers and its
       variants, e.g. reconfigurable and reparameterizable controllers.

     * Stability of learning control systems (important!).

     * Hardware implementations.

     * Applications and case studies which exceed the usual benchmark
       problems towards real-world complex systems.

Questions about, or contributions to this special session can be
e-mailed to Karl Mathia at

  karl at mathia.com
or
  kmathia at accurate-automation.com


PAPER SUBMISSION
----------------
Please mail three (3) hardcopies of your abstract or draft (1-2 pages)
to:

  Dr. Karl Mathia
  Accurate Automation Corporation
  7001 Shallowford Road                 Phone: (423) 894-4646
  Chattanooga, TN 37421                 Fax:   (423) 894-4645
  USA

Full-size papers (max. 8 pages, single spaced) are be submitted by
authors after the notification of acceptance.
Please note the extended deadline for camera-ready papers: May 12, 1997.

DEADLINES
---------
March 15, 1997    Submission of 1-2 page abstracts or drafts.
March 31, 1997    Acceptance notifications.
May 1,    1997    Submission of camera ready papers (max. 8 pages,
                  single spaced).

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