Advertisement

Proactive Continual Planning –

Deliberately Interleaving Planning and Execution in Dynamic Environments
  • Michael Brenner
  • Bernhard Nebel
Chapter
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 76)

Abstract

In order to behave intelligently, artificial agents must be able to deliberatively plan their future actions. Unfortunately, realistic agent environments are usually highly dynamic and only partially observable, which makes planning computationally hard. For most practical purposes this rules out planning techniques that account for all possible contingencies in the planning process. However, many agent environments permit an alternative approach, namely continual planning, i. e. the interleaving of planning with acting and sensing.

This article presents a principled approach to continual planning that describes why and when an agent should switch between planning and acting. The resulting continual planning algorithm enables agents to deliberately postpone parts of their planning process and instead actively gather missing information that is relevant for the later refinement of the plan. To this end, the algorithm explictly reasons about the knowledge (or lack thereof) of an agent and its sensory capabilities. In order to enable proactive information gathering we introduce the concept of assertions into our planning language, i.e. abstract actions that can substitute yet unformed subplans in early planning phases.

To study our continual planning approach empirically we have developed MAPSIM, a simulation environment that automatically builds multiagent simulations from planning domain descriptions. In MAPSIM, agents can thus not only plan, but also execute their plans, perceive their environment, and interact with each other.While obviously such a simulation does not capture many aspect of a physical robot environment, it can be used for rapid prototyping of planning models for such environments.

Keywords

Multiagent System Planning Domain Plan Execution Symbolic Execution Planning Language 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ambros-Ingerson, J.A., Steel, S.: Integrating planning, execution and monitoring. In: Proc. AAAI 1988, Saint Paul, MI, pp. 83–88 (August 1988)Google Scholar
  2. 2.
    Bäckström, C.: Computational aspects of reordering plans. JAIR 9, 99–137 (1998)Google Scholar
  3. 3.
    Bonet, B., Geffner, H.: Planning with incomplete information as heuristic search in belief space. In: Proceedings of the 5th International Conference on Artificial Intelligence Planning Systems (AIPS 2000), pp. 52–61. AAAI Press, Menlo Park (2000)Google Scholar
  4. 4.
    Brenner, M., Nebel, B.: Continual planning and acting in dynamic multiagent environments. Journal of Autonomous Agents and Multiagent Systems 19(3), 297–331 (2009)CrossRefGoogle Scholar
  5. 5.
    DesJardins, M., Durfee Jr., E., Ortiz, C., Wolverton, M.: A survey of research in distributed, continual planning. The AI Magazine 20(4), 13–22 (1999)Google Scholar
  6. 6.
    Erol, K., Hendler, J., Nau, D.: Complexity results for hierarchical task-network planning. Annals of Mathematics and Artificial Intelligence 18, 69–93 (1996)MathSciNetMATHCrossRefGoogle Scholar
  7. 7.
    Etzioni, O., Golden, K., Weld, D.S.: Sound and efficient closed-world reasoning for planning. Artificial Intelligence 89(1-2), 113–148 (1997)MathSciNetMATHCrossRefGoogle Scholar
  8. 8.
    Etzioni, O., Hanks, S., Weld, D., Draper, D., Lesh, N., Williamson, M.: An approach to planning with incomplete information. In: Proc. KR 1992, pp. 115–125 (1992)Google Scholar
  9. 9.
    Golden, K.: Leap before you look: Information gathering in the puccini planner. In: Proc. AIPS 1998, pp. 70–77 (1998)Google Scholar
  10. 10.
    Golden, K., Weld, D.: Representing sensing actions: The middle ground revisited. In: Proc. KR 1996 (1996)Google Scholar
  11. 11.
    Hoffmann, J., Brafman, R.: Contingent planning via heuristic forward search with implicit belief states. In: Biundo, S., Myers, K.L., Rajan, K. (eds.) ICAPS, pp. 71–80. AAAI (2005)Google Scholar
  12. 12.
    Knoblock, C.A.: Planning, executing, sensing, and replanning for information gathering. In: Mellish, C. (ed.) Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, pp. 1686–1693. Morgan Kaufmann, San Francisco (1995)Google Scholar
  13. 13.
    Levesque, H.J.: What is planning in the presence of sensing? In: Proc. AAAI 1996, pp. 1139–1146. MIT Press (July 1996)Google Scholar
  14. 14.
    Meyer, B.: Applying ”Design by Contract”. Computer 25, 40–51 (1992)CrossRefGoogle Scholar
  15. 15.
    Nau, D., Cao, Y., Lotem, A., Munoz-Avila, H.: SHOP: Simple hierarchical ordered planner. In: Dean, T. (ed.) Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI 1999), Stockholm, Sweden, August 1999. Morgan Kaufmann (1999)Google Scholar
  16. 16.
    Nau, D.S., Au, T.-C., Ilghami, O., Kuter, U., William Murdock, J., Wu, D., Yaman, F.: Shop2: An htn planning system. JAIR 20, 379–404 (2003)MATHGoogle Scholar
  17. 17.
    Petrick, R., Bacchus, F.: A knowledge-based approach to planning with incomplete information and sensing. In: Proc. AIPS 2000 (2002)Google Scholar
  18. 18.
    Petrick, R.P.A., Bacchus, F.: Extending the knowledge-based approach to planning with incomplete information and sensing. In: Proc. ICAPS 2004, pp. 2–11 (2004)Google Scholar
  19. 19.
    Rintanen, J.: Complexity of planning with partial observability. In: Proc. ICAPS 2004, pp. 345–354 (2004)Google Scholar
  20. 20.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice-Hall, Englewood Cliffs (2003)Google Scholar
  21. 21.
    Thiebaux, S., Hoffmann, J., Nebel, B.: Defense of axioms in PDDL. In: Proc. IJCAI (2003)Google Scholar
  22. 22.
    Weld, D.S., Anderson, C.R., Smith, D.E.: Extending graphplan to handle uncertainty and sensing actions. In: AAAI/IAAI, pp. 897–904 (1998)Google Scholar
  23. 23.
    Yang, Q.: Intelligent Planning: A decomposition and abstraction based approach. Springer, Heidelberg (1997)MATHGoogle Scholar
  24. 24.
    Yokoo, M., Hirayama, K.: Algorithms for distributed constraint satisfaction: a review. Autonomous Agents and Multi-Agent Systems 3(2) (2000)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Institut für InformatikAlbert-Ludwigs-Universität FreiburgFreiburgGermany

Personalised recommendations