Connectionists: CFP: JMLR Special Topic on Learning in Large Probabilistic Environments

[Sven Koenig]

			 CALL FOR PAPERS
	      Journal of Machine Learning Research

  Special Topic 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 topic 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 topic 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
topic on Learning in Large Probabilistic Environments.  Accepted
papers will be published in JMLR as they become available.

Important Dates:

* Submission due: June 1st, 2005
* Decision: September 1st, 2005
* Final version due: November 1st, 2005 

Early submissions are encouraged, and will be handled immediately
following the submission. 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)