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

[Artur 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 and/or skill
acquisition.
This special 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 special
volume 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 this special issue and the journal.

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, US
URL: http://cobweb.ecn.purdue.edu/~givan/

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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
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