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: March 31, 2008 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 (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 In case this CFP looks familiar: We solicited papers for this special issue earlier this year. To avoid conflicts with relevant conference paper deadlines, we extended the revision period for those papers enough that we can now consider additional novel submissions. All accepted papers will be combined into a single special issue. If you would like to submit, please email us with a brief summary of the paper concept for feedback. 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: March 31, 2008 Send Papers to Reviewers: April 15, 2008 Reviews Due Back to Editors: July 15, 2008 Decisions Announced: August 1, 2008 Camera-Ready Due: September 30, 2008 Print Publication: End of 2008 or early 2009 ---------------------------------------------------------------------- _______________________________________________ uai mailing list uai@ENGR.ORST.EDU https://secure.engr.oregonstate.edu/mailman/listinfo/uai
