Skip to main content
Springer Nature Link
Log in

Sensor Planning with Non-linear Utility Functions

  • Conference paper
Recent Advances in AI Planning (ECP 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1809))

Included in the following conference series:

  • 671 Accesses

Abstract

Sensor planning is concerned with when to sense and what to sense. We study sensor planning in the context of planning objectives that trade-off between minimizing the worst-case, expected, and best-case plan- execution costs. Sensor planning with these planning objectives is interesting because they are realistic and the frequency of sensing changes with the planning objective: more pessimistic decision makers tend to sense more frequently. We perform sensor planning by combining one of our techniques for planning with non-linear utility functions with an existing sensor-planning method. The resulting sensor-planning method is not only as easy to implement as the sensor-planning method that it extends but also (almost) as efficient. We demonstrate empirically how sensor plans change as the planning objective changes, using a common testbed for sensor planning.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Abramson, B.: A decision-theoretic framework for integrating sensors in AI plans. IEEE Transactions on Systems, Man, and Cybernetics 23, 366–373 (1993)

    Article  MATH  Google Scholar 

  2. Boutilier, C., Dean, T., Hanks, S.: Decision-theoretic planning: Structural assumptions and computational leverage. Journal of Artificial Intelligence Research (1999)

    Google Scholar 

  3. Fernandez-Gaucherand, E., Marcus, S.: Risk-sensitive optimal control of hidden Markov models: Structural results. IEEE Transactions on Automatic Control (1997)

    Google Scholar 

  4. Hager, G.: Task-Directed Sensor Fusion and Planning: A Computational Approach. Kluwer Academic Publishers, Dordrecht (1990)

    Google Scholar 

  5. Hansen, E.: Markov decision processes with observation costs. Technical Report CMPSCI 97-01, Department of Computer Science, University of Massachusetts, Amherst, Massachusetts (1997)

    Google Scholar 

  6. Howard, R.: Dynamic Programming and Markov Processes, 3rd edn. MIT Press, Cambridge (1964)

    MATH  Google Scholar 

  7. Howard, R., Matheson, J.: Risk-sensitive Markov decision processes. Management Science 18(7), 356–369 (1972)

    Article  MATH  MathSciNet  Google Scholar 

  8. Koenig, S., Simmons, R.G.: How to make reactive planners risk-sensitive. In: Proceedings of the International Conference on Artificial Intelligence Planning Systems, pp. 293–298 (1994)

    Google Scholar 

  9. Koenig, S., Simmons, R.G.: Risk-sensitive planning with probabilistic decision graphs. In: Proceedings of the International Conference on Principles of Knowledge Representation and Reasoning, pp. 2301–2308 (1994)

    Google Scholar 

  10. Kristensen, S.: Sensor planning with Bayesian decision theory. In: Dorst, L., Voorbraak, F., van Lambalgen, M. (eds.) RUR 1995. LNCS, vol. 1093, pp. 353–367. Springer, Heidelberg (1996)

    Chapter  Google Scholar 

  11. Langley, P., Iba, W., Shrager, J.: Reactive and automatic behavior in plan execution. In: Proceedings of the International Conference on Planning Systems, pp. 299–304 (1994)

    Google Scholar 

  12. Lee, S., Zhao, X.: Sensor planning with hierarchically distributed perception net. In: Proceedings of the International Conference on Multisensor Fusion and Integration for Intelligent Systems, pp. 591–598 (1994)

    Google Scholar 

  13. Lozano-Perez, T., Mason, M., Taylor, R.: Automatic synthesis of fine-motion strate- gies for robots. International Journal of Robotics Research 3(1), 3–24 (1984)

    Article  Google Scholar 

  14. Marcus, S., Fernàndez-Gaucherand, E., Hernàndez-Hernàndez, D., Colaruppi, S., Fard, P.: Risk-sensitive Markov decision processes. In: Byrnes, C., et al. (eds.) Systems and Control in the Twenty-First Century, pp. 263–279. Birkhauser, Basel (1997)

    Google Scholar 

  15. Mine, H., Osaki, S.: Markovian Decision Processes. Elsevier, Amsterdam (1970)

    MATH  Google Scholar 

  16. Nilsson, N.: Problem-Solving Methods in Artificial Intelligence. McGraw-Hill, New York (1971)

    Google Scholar 

  17. Pratt, J.: Risk aversion in the small and in the large. Econometrica 32(1-2), 122–136 (1964)

    Article  MATH  Google Scholar 

  18. Stentz, A., Hebert, M.: A complete navigation system for goal acquisition in unknown environments. Autonomous Robots 2(2), 127–145 (1995)

    Article  Google Scholar 

  19. Tarabanis, K., Allen, P., Tsai, R.: A survey of sensor planning in computer vision. IEEE Transactions on Robotics and Automation 11(1), 86–104 (1995)

    Article  Google Scholar 

  20. Watson, S., Buede, D.: Decision Synthesis. Cambridge University Press, Cambridge (1987)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Georgia Institute of Technology, College of Computing, Atlanta, GA, 30332-0280, USA

    Sven Koenig & Yaxin Liu

Authors
  1. Sven Koenig
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yaxin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute for Artificial Intelligence, Ulm University, Germany

    Susanne Biundo

  2.  ,  

    Maria Fox

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Koenig, S., Liu, Y. (2000). Sensor Planning with Non-linear Utility Functions. In: Biundo, S., Fox, M. (eds) Recent Advances in AI Planning. ECP 1999. Lecture Notes in Computer Science(), vol 1809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10720246_21

Download citation

  • DOI: https://doi.org/10.1007/10720246_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67866-3

  • Online ISBN: 978-3-540-44657-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation