Title :
Artificial intelligence scheduling for NASA´s Hubble Space Telescope
Author :
Johnston, Mark D. ; Miller, Glenn
Author_Institution :
Space Telescope Sci. Inst., Baltimore, MD, USA
Abstract :
An artificial intelligence (AI) system called Spike has been implemented for scheduling observations with NASA´s Hubble Space Telescope. The system incorporates innovative methodologies for representing and reasoning with scheduling constraints and preferences and for conducting scheduling search. For the former, a combination of constraint satisfaction techniques and weight-of evidence combination has been devised to propagate temporal constraints and preferences. For the latter, a neural network scheduling has been found to be highly efficient. The Spike system is operational and is being used to generate long-range schedules for Space Telescope operations to follow initial on-orbit checkout
Keywords :
aerospace computing; artificial intelligence; astronomical telescopes; astronomy computing; scheduling; space research; AI scheduling; Hubble Space Telescope; Space Telescope operations; Spike system; artificial intelligence; constraint satisfaction techniques; long-range schedules; neural network scheduling; on-orbit checkout; preferences; scheduling constraints; scheduling search; temporal constraints; weight-of evidence combination; Artificial intelligence; Drives; Job shop scheduling; NASA; Neural networks; Optimal scheduling; Processor scheduling; Production facilities; Space vehicles; Telescopes;
Conference_Titel :
AI Systems in Government Conference, 1990. Proceedings., Fifth Annual
Conference_Location :
Washington, DC
Print_ISBN :
0-8186-2044-7
DOI :
10.1109/AISIG.1990.63800
