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Abstract
Satellites represent scarce resources that must be carefully scheduled to maximize their value to service consumers. Near-optimal satellite task scheduling is so computationally difficult that it typically takes several hours to schedule one day’s activities for a set of satellites and tasks. Thus, often a requestor will not know if a task will be scheduled until it is too late to accommodate scheduling failures. This paper presents our experiences creating a fast Analogical Reasoning (AR) system and an even faster Case-Based Reasoner (CBR) that can predict, in less than a millisecond, whether a hypothetical task will be scheduled successfully. Requestors can use the system to refine tasks for maximum schedulability. We report on three increasingly narrow approaches that use domain knowledge to constrain the problem space. We show results that indicate the method can achieve >80% accuracy on the given problem.
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Authors and Affiliations
HRL Laboratories, LLC, 3011 Malibu Canyon Road, Malibu, CA, 90265, USA
Pete Tinker & Jason Fox
Formerly HRL Laboratories, LLC,
Chris Furmanski
NASA Ames Research Center,
Collin Green
Raytheon Company,
David Rome & Karen Casey
- Pete Tinker
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- Jason Fox
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- Collin Green
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- David Rome
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- Karen Casey
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- Chris Furmanski
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Editor information
Editors and Affiliations
Department of Computer Science & Engineering, Lehigh University, PA 18015, Bethlehem,
Héctor Muñoz-Ávila
Free University of Bozen-Bolzano,
Francesco Ricci
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© 2005 Springer-Verlag Berlin Heidelberg
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Tinker, P., Fox, J., Green, C., Rome, D., Casey, K., Furmanski, C. (2005). Analogical and Case-Based Reasoning for Predicting Satellite Task Schedulability. In: Muñoz-Ávila, H., Ricci, F. (eds) Case-Based Reasoning Research and Development. ICCBR 2005. Lecture Notes in Computer Science(), vol 3620. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11536406_43
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