Machine Learning in Space: Extending Our Reach

A Special Issue of Machine Learning

Amy McGovern and Kiri L. Wagstaff, guest editors

Call For Papers: Submissions Due: November 15, 2009

Machine learning can be used to significantly expand the capabilities of remote agents operating in space missions. For example, spacecraft could intelligently filter their observations to make the best use of available bandwidth or rovers with learning capabilities could more thoroughly and more quickly explore new environments. Autonomous robots can play a key role in creating a successful human presence on the Moon and Mars, both before humans arrive and in collaboration with them once humans are on site. However, care must be exercised in applying and developing techniques which will truly operate without human intervention. The risks and possible safety implications need to be well understood.

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:

We encourage all prospective authors to email us with a brief summary the paper concept for feedback, especially for surveys 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:


Submission Deadline:November 15, 2009
Send Papers to Reviewers:November 30, 2009
Reviews Due Back to Editors:January 31, 2010
Decisions Announced:February 15, 2010
Camera-Ready Due:April 15, 2010
Print Publication:Mid to late 2010