Skip to main content

SimPlanner: An Execution-Monitoring System for Replanning in Dynamic Worlds

  • Conference paper
  • First Online:
Progress in Artificial Intelligence (EPIA 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2258))

Included in the following conference series:

Abstract

In this paper we present SimPlanner, an integrated planning and execution-monitoring system. SimPlanner allows the user to monitor the execution of a plan, interrupt this monitoring process to introduce new information from the world and repair the plan to get it adapted to the new situation.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bacchus F.: AIPS 2000 competition results. Technical Report, University of Toronto, 2000. http://www.cs.toronto.edu/aips2000/.

  2. Blum A. L., Furst M.L.: Fast Planning Through Planning Graph Analysis. Artificial Intelligence 90:281–300 (1997).

    Article  MATH  Google Scholar 

  3. Despouys O., Ingrand F.F.: Propice-Plan: Toward a Unified Framework for Planning and Execution. European Conference on Planning 99, 280–292, 1999.

    Google Scholar 

  4. Haslum P., Geffner H.: Admissible heuristics for optimal planning. International Conference on AI Planning and Scheduling, 2000.

    Google Scholar 

  5. Hoffmann J., Nebel B.: The FF planning system: fast plan generation through heuristic search. Journal of Artificial Intelligence Research, 14:253–302, 2001.

    MATH  Google Scholar 

  6. Onaindia E., Sebastia L., Marzal E.: Incremental local search for planning problems. ECAI-2000 workshop on local search for planning & scheduling. Lecture Notes in AI, Springer-Verlag, 2001.

    Google Scholar 

  7. Fox M., Long D.: STAN public source code. http://www.dur.ac.uk/CompSci/research/stanstuff/ (1999)

  8. Stone P., Veloso M.: User-guided interleaving of Planning and Execution. Frontiers in Artificial Intelligence and Applications, 103–112, IOS Press, 1996.

    Google Scholar 

  9. Wilkins D.E.: Practical Planning: Extending the Classical AI Planning Paradigm. Morgan Kaufmann Publishers, San Mateo, CA, 1988.

    Google Scholar 

  10. Wilkins D.E., Myers K.L.: Asynchronous Dynamic Replanning in a Multiagent Planning Architecture. Advanced Planning Technology, 267–274, 1996.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Onaindia, E., Sapena, O., Sebastia, L., Marzal, E. (2001). SimPlanner: An Execution-Monitoring System for Replanning in Dynamic Worlds. In: Brazdil, P., Jorge, A. (eds) Progress in Artificial Intelligence. EPIA 2001. Lecture Notes in Computer Science(), vol 2258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45329-6_38

Download citation

  • DOI: https://doi.org/10.1007/3-540-45329-6_38

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43030-8

  • Online ISBN: 978-3-540-45329-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics