Connectionists: Journal of Algorithms thematic issue on Algorithmic Reinforcement Learning (final call for papers)

[Artur d'Avila Garcez]

Call for Papers

Journal of Algorithms, Elsevier
 
http://www.cs.rhul.ac.uk/~kostas/arl/cfp.html
 
Thematic Issue on "Algorithmic Reinforcement Learning"
 
http://www.elsevier.com/locate/jalgor
 
Aim & Scope

Reinforcement learning is an area of machine learning seeking to provide
a computational approach to understanding and automating goal-directed
learning and decision-making. It addresses the question of how an autonomous
agent that senses and acts in its environment can learn to choose optimal
actions to achieve its goals. The approach originates from previous work in
psychology (particularly animal learning), computer science (particularly
dynamic programming), with ongoing work in artificial intelligence
(particularly stochastic, symbolic and connectionist learning). More
recently, reinforcement learning has been used to provide cognitive models
that simulate human performance during problem solving or skill
acquisition.

This thematic issue of the Journal of Algorithms seeks to celebrate the
increasingly multidisciplinary nature of reinforcement learning and, in line
with the Journal's manifesto, it proposes to study and present the subject
from an algorithmic perspective that we refer to as Algorithmic
Reinforcement Learning (ARL). It is hoped in this way that the
issue will serve as a reference in the area, and will help organise and
promote the research across sub-areas.

We welcome the submission of innovative and mature results in specifying,
developing and experimenting with ARL. Approaches that relate, compare and
contrast, combine or integrate different areas of reinforcement learning are
particularly encouraged. Papers describing innovative developments in the
area are also encouraged. Areas of interest include, but are not limited to,
the following topics:
 
 Multi-agent reinforcement learning
 Relational reinforcement learning
 Neuro-symbolic reinforcement learning
 Bayesian reinforcement learning
 Reinforcement learning and logic/ILP
 Reinforcement learning with background knowledge
 Robust reinforcement learning
 Reinforcement learning in game theory and bounded rationality
 Applications
 
Important Dates
 
 Submission Deadline: 1st October 2008
 Acceptance Notice: 20th January 2009
 Final Manuscript: 1st March 2009
 Publication Date: 2nd Quarter, 2009 (tentative)
 
Submission Guidelines

The work submitted must be in the form of high quality, original
papers, which are not simultaneously submitted for publication elsewhere.
Papers should be formatted according to the journal style, and not exceed
25 pages including figures, references, etc. The papers must be submitted
by sending a PDF version of the complete manuscript to:

arl-guest-eds at cs.rhul.ac.uk

Submitted papers will be peer reviewed according to their originality,
quality and relevance to the thematic issue.
 
Guest Editors
 
 Dr. Kostas Stathis
 Computer Science Department,
 Royal Holloway, University of London, UK
 URL: http://www.cs.rhul.ac.uk/~kostas

 Dr. Artur d'Avilla Garcez
 Computing Department,
 City University London, UK
 URL: http://www.soi.city.ac.uk/~aag
 
 Dr. Robert Givan
 Department of Electrical and Computer Engineering,
 Purdue University, USA
 URL: http://cobweb.ecn.purdue.edu/~givan/
 
 -----------------------------------------------------------------------
 Dr. Artur d'Avila Garcez
 Reader in Computing
 
 Department of Computing, School of Informatics
 City University London, EC1V 0HB, UK
 Tel: + 44 (0)20 7040 8344  Fax: + 44 (0)20 7040 0244
 Email: aag at soi.city.ac.uk  URL: http://www.soi.city.ac.uk/~aag
 -----------------------------------------------------------------------