CFP: JMLR Special Issue on "Learning in Large Probabilistic Environments"

[Sven Koenig]

			 CALL FOR PAPERS
	      Journal of Machine Learning Research

  Special Issue on Learning in Large Probabilistic Environments

			  Guest Editors
	Sven Koenig, Shie Mannor and Georgios Theocharous

		http://www.jmlr.org/cfp/llpe.html

We invite papers on learning in large probabilistic environments
for a special issue of the Journal of Machine Learning Research
(JMLR). One of the fundamental problems of artificial
Intelligence is how to enable systems (for example, mobile
robots, manufacturing systems, or diagnostic systems) embedded in
complex environments to achieve their long-term goals
efficiently. A natural approach is to model such systems as
agents that interact with their environment through actions,
perceptions and rewards. These agents choose actions after every
observation, aiming to maximize their long-term reward. Learning
allows them to improve their initial strategy based on the
history of successful and unsuccessful interactions with the
environment.

This special issue is intended to serve as an outlet for recent
advances in learning in such environments, often called
reinforcement learning. We welcome both theoretical advances in
this field as well as detailed reports on applications of
learning in large probabilistic domains.

Topics of interest include:

* Theoretical foundations of learning in large probabilistic
  environments.

* Completely and partially observable Markov decision process
  models (MDPs) and similar models. Learning with factored state or
  action spaces, continuous state spaces, action spaces or time
  models, hybrid models, relational learning, concurrency.

* Heuristics and approximations. Policy and value function
  approximations, Monte Carlo and advanced simulation methods.

* Spatio-temporal abstractions. Dynamic factorization, hierarchy
  and relational structure.

* Interactive learning. Guided exploration, combining supervised
  and unsupervised learning, shaping, and learning from very few
  examples.

* Learning in complex systems. Function approximation,
  dimensionality reduction, feature selection for learning, and
  alternative state representations.

* Cooperative and competitive multi-agent reinforcement
  learning. Learning in nonstationary domains and stochastic,
  network, and dynamic games.

* Real world applications. Medicine, finance, robotics,
  manufacturing, security, etc.

Submission procedure:

Submit papers to the standard JMLR submission system

http://jmlr.csail.mit.edu/manudb

Please include a note stating that your submission is for the
special issue on Learning in Large Probabilistic Environments.

Important Dates:

* Submission due: April 15th, 2005
* Decision: August 1st, 2005
* Final version due: October 1st, 2005

For further details or enquiries, please contact the guest
editors:

Sven Koenig (skoenig at usc.edu)
Shie Mannor (shie at ece.mcgill.ca)
Georgios Theocharous (georgios.theocharous at intel.com)