[alife] 2nd CFP: KES 2005 session on Evol. and Self-Organizing Sensors, Actuators, Processing

[Daniel Polani]

Apologies if you receive this call twice. Please forward the call to
all possible interested parties.

Thank you very much in advance.

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		 2nd Call for Papers & Participation:

	      Evolutionary and Self-Organizing Sensors,
		  Actuators and Processing Hardware

			  Invited Session at

			       KES 2005

		  Ninth International Conference on
    Knowledge-Based Intelligent Information & Engineering Systems

	     14-16. September 2005, Melbourne, Australia

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			    Program Chairs

	   Daniel Polani (University of Hertfordshire, UK)
		Mikhail Prokopenko (CSIRO, Australia)

Session website:    http://homepages.feis.herts.ac.uk/~comqdp1/kes_2005.html
Conference website: http://www.latrobe.edu.au/kes/

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Introduction

Recent technology has witnessed the advent of cheap ubiquitous
sensing, processing and actuating capabilities for isolated,
distributed or collective robotic systems. These appear in the form of
intelligent materials, nano-motors and -sensors,
Micro-Electro-Mechanical Systems (MEMS), grid processors,
Avogadro-scale digital circuits and similar structures. Established
conventional AI computation paradigms do not harness the full
potential of this new type of technological ability that includes
dynamic reconfiguration, addition or removal of sensors, actuators or
processing hardware. Classical AI paradigms are inadequate to deal
with the requirements of these scenarios which require flexible and
adaptive acquisition, manipulation and distribution of information as
opposed to sterile off-line AI software designs detached from concrete
usage scenarios.

One is confronted with the necessity to adapt sensoric properties
and/or configuration to a situation or task at hand, discovery of new
sensoric modalities,the use of newly added actuators in novel ways,
the necessity of reconfiguring computational hardware after being
damaged, and much more. What all these requirements have in common is
that, in general, there cannot be a full a priori appreciation of the
possible scenarios that can occur during the lifetime of the involved
hardware and software.

On the other hand, biological systems are capable to tackle such
problems on a regular basis. E.g. the recovery of functionality in
experiments where sensoric or neural tissues are transplanted to other
than the original locations show that biological systems have a
powerful potential to reconfigure their "hardware" and "software" to
suit the relevant situation. Biologically inspired approaches, e.g.
evolutionary and neural methods, as well as self-organization to
tackle these challenges, have been increasingly found to be fruitful.
Evolutionary sensorics, self-organizing controllers, neural strategies
have all provided new insights, methodologies, towards the achievement
of self- and externally modified sensomotoric loops.

Solving these problems has an enormous potential: it would allow the
construction of robust, cheap autonomous vehicles, sensor/actuator
networks consisting of a large number of autonomous sensor/actuator
units ('agents') that interact with each other to obtain the best
results. It would open the way to apply novel sensing/actuation
materials for the construction of agents because the self-organized
adaptation mechanisms would be able to deal with the novelty.
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Call for Contributions

We solicit papers for poster or oral presentations (20 minute talk)
reporting working in this exciting area. Talks should address an
interdisciplinary audience, but may nevertheless deal with issues at
the cutting edge of research.
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Topics

Possible topics for the invited session are or involve (this is not an
exhaustive list):

     * evolution or self-organization of physical sensors and actuators
       (artificial, bio-inspired, and biological)
     * abstract models for the evolution, self-organization and
       adaptation of sensors, actuators and processing, and for detection
       of emergent behaviour
     * evolution of controllers (including, but not limited to neural or
       cellular architectures)
     * self-monitoring and self-repair of damaged sensoric, computational
       and communication architectures
     * self-organization in sensomotoric loops
     * self-organized adaptive communication (e.g. mechanisms for the
       emergence of communication protocols)
     * evolution or self-organized modularity and hierarchies
     * identification of relevant information and features in sensoric
       input and of relevant behaviours and activities in actuatoric
       output

If you are unsure whether your topic is adequate for submission to the
session, please contact the program chairs.
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Important Dates

Submission of papers:       4 March 2005
Notification of acceptance: 15 April 2005

Note: All presenting authors (both General and Special/Invited
Sessions) must register with payment by 1 June 2005, for their papers
to appear in the proceedings (see main KES 2005 Website,
http://www.latrobe.edu.au/kes/).
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Format

The conference proceedings will be published by Springer-Verlag in
Lecture Notes in AI as part of the LNCS/LNAI series. Please refer to
the Springer-Verlag web site for directions on the Paper Format which
must be strictly followed. All oral and poster papers must be
presented by one of the authors who must register and pay fees.

Please note that papers should be no longer than 7 pages. Papers
longer than this will be subject to an additional page charge.

For more information, see the KES 2005 Submission page
(http://www.latrobe.edu.au/kes/index.html#Submission_of_Papers).
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Program Committee

Peter Dauscher          University of Mainz, Germany
Kerstin Dautenhahn      University of Hertfordshire, UK
Andrew Jennings         RMIT, Australia
Hod Lipson              Cornell University, USA
Julian Miller           University of York, UK
Chrystopher Nehaniv     University of Hertfordshire, UK
David Payton            Hughes Research Labs, USA
Don Price               CSIRO, Australia
William Prosser         NASA LaRC, USA
Claude Sammut           UNSW, Australia
Mary-Anne Williams      UTS, Australia