Call For Papers: Submissions Due: November 15, 2009
A Special Issue of Machine Learning
Amy McGovern and Kiri L. Wagstaff, guest editors
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:
• How to perform machine learning in a high-risk, remote environment
• Learning with resource constraints (computation, memory, etc.)
• Multi-instrument machine learning
• Multi-mission machine learning
• Novel applications and uses of on-board machine learning
• On-board machine learning for computer vision and image analysis
• Prioritizing or subsampling data for downlink
• Active selection of new observational targets
• 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
For more details, see: http://www.wkiri.com/ml4space/
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Dr. Amy McGovern
Assistant Professor
School of Computer Science
University of Oklahoma
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