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Title: Robust local search for spacecraft operations using adaptive noise
Authors: Fukunaga, Alex S.
Rabideau, Gregg
Chien, Steve
Keywords: planning
machine learning
Issue Date: 23-Jun-2004
Publisher: Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2004.
Citation: International Workshop on Planning and Schedule for Space, Darmstadt, Germany, June 23-25, 2004
Abstract: Randomization is a standard technique for improving the performance of local search algorithms for constraint satisfaction. However, it is well-known that local search algorithms are constraints satisfaction. However, it is well-known that local search algorithms are to the noise values selected. We investigate the use of an adaptive noise mechanism in an iterative repair-based planner/scheduler for spacecraft operations. Preliminary results indicate that adaptive noise makes the use of randomized repair moves safe and robust; that is, using adaptive noise makes it possible to consistently achieve, performance comparable with the best tuned noise setting without the need for manually tuning the noise parameter.
Appears in Collections:JPL TRS 1992+

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