The deadline for this special issue is one month away. We encourage you
to contact us now with your paper ideas, and we look forward to receiving
your full submissions. Cheers!

------------------------------------------------------------
Machine Learning in Space: Extending Our Reach
Special Issue of the Machine Learning Journal

Amy McGovern and Kiri L. Wagstaff, guest editors

URL: http://www.wkiri.com/ml4space
Submission deadline: July 1, 2007

Remote space environments simultaneously present significant challenges to
the machine learning community and enormous opportunities for advancement.
Enhancing spacecraft autonomy with machine learning has the potential to
permit new discoveries that pre-scripted activities would preclude.
On-board machine learning could enable intelligent filtering or
prioritizing of data as it is collected to make the best use of the
available bandwidth. Rovers with learning capabilities could more
thoroughly and more quickly explore new environments, relating them to
previously observed areas and highlighting novel or unexpected
observations. While some initial tests have been made in this direction,
the increasing computational power now available on spacecraft has
broadened the field of what could feasibly be done on-board. Ultimately,
machine learning can help these spacecraft graduate from their current
status as "science prosthetics" into "science assistants".

The purpose of this special issue is to collect recent advances in machine
learning for remote space or planetary environments and to identify novel
space applications where machine learning could significantly increase
capabilities, robustness, and/or efficiency.

Key topics of interest include:
- How to perform machine learning in a high-risk, remote environment
- Learning with resource constraints (memory, computation, etc.)
- Multi-instrument machine learning
- Multi-mission machine learning
- Novel applications and uses of machine learning in space
- How to evaluate and validate machine learning methods prior to
deployment on-board a spacecraft
- Methods for safe real-time learning
- Methods that trade off exploration and exploitation, given mission
science goals and safety/reliability requirements
- Methods for reducing risk and increasing acceptance of machine
learning in space flight missions
- A survey of space-borne machine learning accomplishments

We encourage all prospective authors to email us with a brief summary of
the paper concept for feedback, especially for survey papers or papers
focused on applications.

Submissions are expected to represent high-quality, significant
contributions in the area of machine learning algorithms and/or
applications. Authors should follow standard formatting guidelines for
Machine Learning manuscripts.

Administrative notes:
* Authors retain the copyrights to their papers. (See publication
agreement on the MLJ website:
http://pages.stern.nyu.edu/~fprovost/MLJ/.)
* Submissions and reviewing will be handled electronically using
standard procedures for Machine Learning (http://mach.edmgr.com).
* Authors must register with the system before they can submit their
manuscripts.
* Authors must select the appropriate Article Type -- Machine
Learning in Space -- when submitting their manuscripts.
* Accepted papers will be published electronically and citable
immediately (before the print version appears).

Schedule
Submission Deadline: July 1, 2007
Send Papers to Reviewers: July 15, 2007
Reviews Due Back to Editors: September 1, 2007
Decisions Announced: September 15, 2007
Camera-Ready Due: October 31, 2007
Print Publication: Early 2008

----------------------------------------------------------------------

--
------ Kiri Wagstaff, Ph.D. ------ kiri.wagstaff@... ------
Senior Researcher at the Jet Propulsion Laboratory
Machine Learning and Instrument Autonomy: http://ml.jpl.nasa.gov/
--------------------------------------------------------------------