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 ([EMAIL PROTECTED])
Shie Mannor ([EMAIL PROTECTED])
Georgios Theocharous ([EMAIL PROTECTED])
_______________________________________________
uai mailing list
uai@ENGR.ORST.EDU
https://secure.engr.oregonstate.edu/mailman/listinfo/uai
