Call for Papers: Machine Learning Journal special issue on REINFORCEMENT LEARNING

[satinder singh baveja]

		Machine Learning Journal

	Special Issue on REINFORCEMENT LEARNING

		Edited by Satinder Singh

	     Submission Deadline: March 1, 1999 

	Reinforcement learning has become an exciting focus for
research in machine learning and artificial intelligence. Some of this
new excitement comes from a convergence of computational approaches to
planning and learning in large-scale environments, some from
accumulating empirical evidence that reinforcement learning algorithms
can solve significant real-world problems, and some from the
ever-increasing mathematical maturity of this field.  In this special
issue we would like to capture a snapshot of this recent
excitement, and invite submission of papers describing new work in
reinforcement learning. New algorithms, problem frameworks,
theoretical results, and empirical results are all appropriate
contributions.


Submission Deadline: March 1, 1999
Expected publication Date: January, 2000


SUBMISSION INSTRUCTIONS
***********************

Submissions will undergo the standard Machine Learning journal review
process. 

Send one hard-copy of submissions to:

	Satinder Singh
	AT&T Shannon Lab
	Room A269
	180 Park Avenue
	Florham Park, NJ 07932
	USA

	Fax:	(973) 360-8970
	E-mail:	baveja at research.att.com

	If possible, e-mail the abstract separately to
baveja at research.att.com, even before it is final, so that reviewers
can be allocated efficiently.

Also mail five hard-copies of submitted papers to:

	Karen Cullen
	MACHINE LEARNING Editorial Office
	Kluwer Academic Publishers
	101 Philip Drive
	Assinippi Park
	Norwell, MA 02061 
	USA

	phone:	(617) 871-6300
	E-mail:	karen at world.std.com

**** PLEASE INDICATE that your submission is for the REINFORCEMENT
**** LEARNING SPECIAL issue.

Submissions exceptionally longer than 24 pages when formatted
according the journal's style (pointers to which are below) may be
rejected without review.

Note: Machine Learning is now accepting submission for FINAL copy in
electronic form. There is a latex style file and a related files
available via the url: http://www.wkap.nl/journalhome.htm/0885-6125
Authors are strongly encouraged to use these style files or the
formatting instructions stated at the back of the Machine Learning
journal for their submissions.