Artificial Intelligence Approaches to Astronomical Observation Scheduling

  • Mark D. Johnston
  • Glenn Miller
Part of the Ettore Majorana International Science Series book series (EMISS, volume 40)


Automated scheduling will play an increasing role in future ground-and space-based observatory operations. Due to the complexity of the problem, artificial intelligence technology currently offers the greatest potential for the development of scheduling tools with sufficient power and flexibility to handle realistic scheduling situations. This paper summarizes the main features of the observatory scheduling problem, how AI techniques can be applied, and recent progress on AI scheduling for Hubble Space Telescope.


Schedule Problem Hubble Space Telescope Partial Schedule Schedule Constraint Artificial Intelligence Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [1]
    King, J.R., and Spachis, A.S. 1980: “Scheduling: Bibliography and Review,” Int. Journal of Physical Distribution and Materials Management 10, p. 105.Google Scholar
  2. [2]
    Garey, M., and Johnson, D. 1979: Computers and Intractability ,(W.H. Freeman &Co.: San Francisco).MATHGoogle Scholar
  3. [3]
    Fox, M, and Smith, S. 1984: “ISIS: A Knowledge-Based System for Factory Scheduling,” Expert Systems 1, p. 25.CrossRefGoogle Scholar
  4. [4]
    Smith, S., Fox, M., and Ow, P. 1986: “Constructing and Maintaining Detailed Construction Plans,” AI Magazine, Fall 1986, p. 45Google Scholar
  5. [5]
    Miller, G., Rosenthal, D., Cohen, W., and Johnston, M. 1987: “Expert System Tools for Hubble Space Telescope Observation Scheduling,” in Proc. 1981 Goddard Conference on Space Applications of Artificial Intelligence ,reprinted in Telematics and Infomatics 4, p. 301 (1987).Google Scholar
  6. [6]
    Johnston, M., 1988: “Automated Telescope Scheduling,” in Proc. Conf. on Coordination of Observational Projects ,Strasbourg, Nov. 1987, in press.Google Scholar
  7. [7]
    Miller, G., Johnston, M., Vick, S., Sponsler, J., and Lindenmayer, K. 1988: “Knowledge Based Tools for Hubble Space Telescope Planning and Scheduling: Constraints and Strategies”in Proc. 1988 Goddard Conference on Space Applications of Artificial Intelligence.Google Scholar
  8. [8]
    “HST Planning Constraints”1987, Space Telescope Science Institute, SPIKE Report 87–1.Google Scholar
  9. [9]
    Johnston, M. 1988: “Reasoning with Scheduling Constraints and Preferences,” in preparation.Google Scholar
  10. [10]
    Johnston, M., and Adorf, H.-M. 1988: “Scheduling with Neural Networks”in preparation.Google Scholar
  11. [11]
    Shortliffe, E. 1987: Computer-Based Medical Consultations: MYCIN (American Elsevier: New York).Google Scholar
  12. [12]
    Duda, R., Gaschnig, J., and Hart, P. 1980: “Model design in the Prospector consultant system for mineral exploration”in Expert Systems in the Microelectronic Age ,ed. Michie, D. (Edinburgh University Press).Google Scholar
  13. [13]
    Hopfield, J., and Tank, D. 1985: “Neural Computation of Decisions in Optimization Problems,” Biological Cybernetics 52, p. 141.Google Scholar
  14. [14]
    Johnston, M. 1988: “Automated Observation Scheduling for the VLT”in Proc. ESO Conference on Very Large Telescopes and their Instrumentation ,Garching, March 1988.Google Scholar
  15. [15]
    Fosbury, R.A.E., Adorf, H.-M., and Johnston, M. 1988: “VLT Operations -the Astronomers’ Environment,” in Proc. ESO Conference on Very Large Telescopes and their Instrumentation ,Garching, March 1988.Google Scholar

Copyright information

© Plenum Press, New York 1989

Authors and Affiliations

  • Mark D. Johnston
    • 1
  • Glenn Miller
    • 2
  1. 1.Space Telescope Science InstituteBaltimoreUSA
  2. 2.Astronomy ProgramsComputer Sciences CorporationUSA

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