Connectionists: Call for Papers - Neural Networks special issue on Autonomous Learning

[Asim Roy]

Apologies for cross posting.

 

Neural Networks Special Issue: Autonomous Learning
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Autonomous learning is a very broad term and includes many different
kinds of learning. Fundamental to all of them is some kind of a learning
algorithm. Whatever the kind of learning, we generally have not been
able to deploy the learning systems on a very wide scale, although there
certainly are exceptions. 

 

One of the biggest challenges to wider deployment of existing learning
systems comes from algorithmic control. Most of the current learning
algorithms require parameters to be set individually for almost every
problem to be solved. The limitations of the current learning systems,
compared to biological ones, was pointed out in a 2007 National Science
Foundation (USA) report
((<http://www.cnl.salk.edu/Media/NSFWorkshopReport.v4.pdf
<http://www.cnl.salk.edu/Media/NSFWorkshopReport.v4.pdf> >). Here's a
part of the summary of that report:

"Biological learners have the ability to learn autonomously, in an ever
changing and uncertain world. This property includes the ability to
generate their own supervision, select the most informative training
samples, produce their own loss function, and evaluate their own
performance. More importantly, it appears that biological learners can
effectively produce appropriate internal representations for composable
percepts -- a kind of organizational scaffold - - as part of the
learning process. By contrast, virtually all current approaches to
machine learning typically require a human supervisor to design the
learning architecture, select the training examples, design the form of
the representation of the training examples, choose the learning
algorithm, set the learning parameters, decide when to stop learning,
and choose the way in which the performance of the learning algorithm is
evaluated. This strong dependence on human supervision is greatly
retarding the development and ubiquitous deployment autonomous
artificial learning systems."

This special issue of Neural Networks will be on the topic of autonomous
learning, focusing mainly on automation of learning methods that can
avoid the kinds of dependencies highlighted in the NSF report. We invite
original and unpublished research contributions on algorithms for any
type of learning problem. 

 

RECOMMENDED TOPICS:

Topics of interest include - but are not limited to: 

* Unsupervised learning systems;

* Autonomous learning of reasoning;

* Autonomous learning of motor control;

* Autonomous control systems and free will;

* Autonomous robotic systems;

* Autonomy as based on internal reward and value systems and their
learning and development;

* Autonomous systems and the human situation

* Emergent models of perception, cognition and action

* Emergent cognitive architectures
* Developmental and embodied models of learning



SUBMISSION PROCEDURE:

Prospective authors should visit http://ees.elsevier.com/neunet/
<http://ees.elsevier.com/neunet/>  for information on paper submission.
On the first page of the manuscript as well as on the cover letter,
indicate clearly that the manuscript is submitted to the Neural Networks
Special Issue: Autonomous Learning. Manuscripts will be peer reviewed
using Neural Networks guidelines.

 

Manuscript submission due: January 1, 2012

First review completed: April 1, 2012

Revised manuscript due: June 1, 2012 

Second review completed, final decisions to authors: July 1, 2012 

Final manuscript due: August 1, 2012

 

Guest editors:

Asim Roy, Arizona State University, USA (asim.roy at asu.edu
<mailto:asim.roy at asu.edu> ) (Lead guest editor)

John Taylor, King's College London, UK (john.g.taylor at kcl.ac.uk
<mailto:john.g.taylor at kcl.ac.uk>  )

Bruno Apolloni, University of Milan, Italy (apolloni at dsi.unimi.it
<mailto:apolloni at dsi.unimi.it>  ) 

Leonid Perlovsky, Harvard University and The Air Force Research
Laboratory, USA (leonid at seas.harvard.edu
<mailto:leonid at seas.harvard.edu>  )

Ali Minai, University of Cincinnati, USA (minaiaa at gmail.com
<mailto:minaiaa at gmail.com> )

 

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