Skip to main content
SpringerLink
Log in

The roles of humans and computers in distributed planning for dynamic domains

  • Original Article
  • Published:
Cognition, Technology & Work Aims and scope Submit manuscript

Abstract

The requirements and issues associated with computational representations for planning extend beyond those apparent in real-time control, where a substantial, existing research literature informs designers. To assist in the identification of requirements for planning representations, this paper provides two resources: (1) a theoretical foundation drawn from computer science and (2) illustrations of representations and corresponding work practice for real-time control and planning for the US Shuttle program. Together, these resources illustrate the human role in the planning process, and the need for work practices and information that combine to assist human operators in interpreting a representation that is loosely coupled to the physical world while shared among and modified by multiple participants in the planning process.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. Arranging for additional satellite access is only done when absolutely necessary. The satellites are a scarce resource, which can always be deployed for other, possibly revenue generating, purposes.

  2. The orbiter itself also uses representations, of its own location, the location of targets and communication satellites. FDOs also maintain these models, although this activity will not be discussed further here.

  3. The distinction between TDRS ephemerides and other vehicle ephemerides reflects computational limitations of the legacy Mission Operations Computer (MOC), which could not represent TDRS ephemerides with the same fidelity as other vehicles and still maintain acceptable performance.

  4. The use of MET in TPS (and AntMan as well) reflects the existence of a pre-flight planning process. Pre-flight planning provides a point of departure for in-flight adjustments. While planned launch dates and times are subject to uncertainty, the relative sequence and timing of events is quite a bit more certain. By scheduling with respect to MET, preflight planning can proceed despite the uncertainty in launch time. For some long activities such as EVA that may not begin at the expected MET, planners use a third time referent, Phase Elapsed Time (PET). By tying steps of the EVA to PET, planning for the phase can proceed without knowing exactly when the phase will begin.

  5. Contingency plans include maneuvers that will probably not be executed. Contingencies are prepared in advance because the planning process is too slow to create an appropriate plan once the triggering conditions appear.

  6. Shalin and McCraw (2003) recommend replacing the TUP counter with time-elapsed since update, so that users need not remember prior TUP counts in order to detect a change.

  7. In response to concern expressed by the observer, newer versions of the ephemeris panels included a comment line (Shalin and McCraw 2003). However, the comment line requires maintenance, and poses another opportunity for misidentification. Some form of self-documentation, linking operations on the model to commentary is under investigation.

References

  • Agre PE (1995) Computational research on interaction and agency. Artif Intell 72:1–52

    Article  Google Scholar 

  • Bennett KB, Toms ML, Woods DD (1993) Emergent features and graphical elements—designing more effective configural displays. Hum Factors 35(1):71–97

    Google Scholar 

  • Bickhard MH, Terveen L (1995) Foundational issues in artificial intelligence and cognitive science: impasse and solution. Elsevier, Amsterdam

    Google Scholar 

  • Brooks R (1991) Intelligence without representation. Artif Intell 47:139–159

    Article  Google Scholar 

  • Calcaterra JA, Bennett KB (2003) The placement of digital values in configural displays. Displays 24(2):85–96

    Article  Google Scholar 

  • Clancey WJ (1985) Heuristic classification. Artif Intell 27:289–350

    Article  Google Scholar 

  • Degani A, Shafto M, Kirlik A (2000) Modes in human–machine systems: constructs, representation and classification. Int J Aviat Psychol 9(2):125–138

    Google Scholar 

  • deKleer J (1986) An assumption-based TMS. Artif Intell 28:127–162

    Article  Google Scholar 

  • Dubois D, Shalin VL (1995) Job knowledge test design: cognitive contributions to content oriented methods. In: Nichols P, Chipman S, Brennan R (eds) Alternative diagnostic assessment. Lawrence Erlbaum Associates, Hillsdale, pp 189–220

    Google Scholar 

  • Fetterman DM (1998) Ethnography: step by step (2nd edn). Sage Publications, Thousand Oaks

    Google Scholar 

  • Fetzer JH (1997) Thinking and computing: computers as special kinds of signs. Minds Mach 7(3):345–364

    Article  Google Scholar 

  • Georgeff MP (1987) Planning. Annu Rev Comp Sci 2:359–400

    Article  Google Scholar 

  • Hoc JM (2000) From human–machine interaction to human–machine cooperation. Ergonomics 43(7): 833–843

    Article  CAS  PubMed  Google Scholar 

  • Hoc JM (2001) Towards a cognitive approach to human–machine cooperation in dynamic situations. Int J Hum Comp Stud 54:509–540

    Article  Google Scholar 

  • Hutchins E (1995) Cognition in the wild. MIT Press, Cambridge MA

    Google Scholar 

  • Layton C, Smith PJ, McCoy CE (1994) Design of a cooperative problem solving system for en-route flight planning. Hum Factors 36:94–119

    Google Scholar 

  • McCarthy J, Hayes PJ (1969) Some philosophical problems from the standpoint of artificial intelligence. In: Meltzer B, Michie D (eds) Machine Intelligence 4. Endinburgh University Press, Edinburgh, pp 463–502

    Google Scholar 

  • McDermott D (1981) Artificial intelligence meets natural stupidity. In: Haugeland J (ed) Mind Design. MIT Press, pp 143–160

  • Newell A (1980) Physical symbol systems. Cogn Sci 4(2):135–183

    Article  Google Scholar 

  • Newell A, Simon HA (1972) Human problem solving. Prentice-Hall, Englewood Cliffs NJ

    Google Scholar 

  • Norman DA (1991) Cognitive artifacts. In: Carroll J (ed) Designing interaction. Cambridge University Press, Cambridge, pp 17–38

    Google Scholar 

  • Oviatt S (1997) Multimodal interactive maps: Designing for human performance. Hum Comp Interact 12:93–129

    Article  Google Scholar 

  • Sarter NB, Woods DD (1995) How in the world did we ever get in that mode? Mode error and awareness in supervisory control. Hum Factors 37(1):5–19

    Google Scholar 

  • Searle, John R (1997) Minds, brains, and programs. pp 183–204 Mind design 2: philosophy, psychology, artificial intelligence (2nd rev. and enlarged ed.). The MIT Press, Cambridge, MA, US viii, 476pp

  • Shalin VL, McCraw PM (2003) Representations for distributed planning. In: Hollnagel E (ed) Handbook of cognitive task design. Lawrence Erlbaum Associates, New Jersey, pp 701–725

    Google Scholar 

  • Shehory O, Kraus S, Yadgar O (1999) Emergent cooperative goal-satisfaction in large-scale automated-agent systems. Artif Intell 110:1–55

    Article  Google Scholar 

  • Smith PJ, McCoy E, Layton C (1997) Brittleness in the design of cooperative problem-solving systems: the effects on user performance. IEEE Trans Syst Man Cybern 27(3):360–371

    Article  Google Scholar 

  • Vicente KJ, Burns CM (1996) Evidence for direct perception from cognition in the wild. Ecol Psychol 8(3):269–280

    Google Scholar 

  • Wickens CD (1992) Engineering psychology and human performance. Harper Collins, New York

    Google Scholar 

  • Wickens CD, Haskell I, Harte K (1989) Ergonomic design for perspective flight-path displays. IEEE Control Syst Mag 9(4):3–8

    Article  Google Scholar 

  • Winograd T (1990) Thinking machines: Can there be? Are we? In: Partridge D, Wilks Y (eds) The foundations of artificial intelligence: a sourcebook. Cambridge University Press, Cambridge

  • Winograd T, Flores F (1986) Understanding computers and cognition. Ablex Corporation, Norwood

    Google Scholar 

  • Woods DD (1988a) Coping with complexity: the psychology of human behaviour in complex systems. In: Goodstein LP, Andersen HB, Olson SE (eds) Tasks, errors and mental models. Taylor and Francis, London, pp 128–148

    Google Scholar 

  • Woods DD (1988b) Commentary: Cognitive engineering in complex and dynamic worlds. In: Hollnagel E, Mancini G, Woods DD (eds) Cognitive engineering in complex dynamic worlds. Academic, London, pp 115–129

    Google Scholar 

Download references

Acknowledgements

This project has been supported under cooperative agreement NAG2-1237 with the Human Centered Computation program in the Information Sciences division at NASA Ames Research Center. The first author is grateful for invaluable opportunities and guidance under this agreement from the program director, William J. Clancey. Several students participated in setting up equipment, organizing recordings and transcribing some of the audio: Scott Bachmann, Rodney Halgren, Judith Isaacson, Paul Jacques, James Kondash, Mark Palumbo, Louise Rasmussen and Brian Simpson. I am profoundly grateful for the exceptional cooperation and contributions of the Orbital Dynamics and Instrumentation and Communications sections in the Mission Operations Directorate at NASA’s Johnson Space Center, under the direction of Pam McCraw, Joseph Williams and Sarah Murray. The views and conclusions presented in this paper are those of the author and do not represent an official opinion, expressed or implied, of the sponsoring agency or the acknowledged individuals.

Author information

Authors and Affiliations

  1. Wright State University, Dayton, OH, USA

    Valerie L. Shalin

Authors
  1. Valerie L. Shalin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Valerie L. Shalin.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shalin, V.L. The roles of humans and computers in distributed planning for dynamic domains. Cogn Tech Work 7, 198–211 (2005). https://doi.org/10.1007/s10111-005-0186-2

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10111-005-0186-2

Keywords

Search

Navigation