Hybrid Planning Using Flexible Strategies

  • Bernd Schattenberg
  • Andreas Weigl
  • Susanne Biundo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3698)

Abstract

In this paper we present a highly modular planning system architecture. It is based on a proper formal account of hybrid planning, which allows for the formal definition of (flexible) planning strategies. Groups of modules for flaw detection and plan refinement provide the basic functionalities of a planning system. The concept of explicit strategy modules serves to formulate and implement strategies that orchestrate the basic modules. This way a variety of fixed plan generation procedures as well as novel flexible planning strategies can easily be implemented and evaluated. We present a number of such strategies and show some first comparative performance results.

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References

  1. 1.
    Castillo, L., Fdez-Olivares, J., González, A.: On the adequacy of hierarchical planning characteristics for real-world problem solving. In: Proc. of 6th European Conference on Planning (ECP 2001) (2001)Google Scholar
  2. 2.
    Biundo, S., Schattenberg, B.: From abstract crisis to concrete relief – A preliminary report on combining state abstraction and HTN planning. In: Cesta, A., Borrajo, D. (eds.) Proc. of 6th European Conference on Planning, ECP 2001 (2001)Google Scholar
  3. 3.
    Estlin, T.A., Chien, S.A., Wang, X.: An argument for a hybrid HTN/operator-based approach to planning. In: Steel, S., Alami, R. (eds.) ECP 1997. LNCS (LNAI), vol. 1348, pp. 182–194. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  4. 4.
    Tsuneto, R., Nau, D., Hendler, J.: Plan-refinement strategies and search-space size. In: Steel, S. (ed.) ECP 1997. LNCS, vol. 1348, pp. 414–426. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  5. 5.
    McCluskey, T.L.: Object transition sequences: A new form of abstraction for HTN planners. In: Chien, S., Kambhampathi, R., Knoblock, C. (eds.) Proc. of 5th International Conference on Artificial Intelligence Planning Systems (AIPS 2000), pp. 216–225. AAAI, Menlo Park (2000)Google Scholar
  6. 6.
    Nau, D., Cao, Y., Lotem, A., Munoz-Avila, H.: SHOP: Simple hierarchical ordered planner. In: Dean, T. (ed.) Proc. of 16th International Joint Conference on Artificial Intelligence (IJCAI 1999), pp. 968–975. Morgan Kaufmann, San Francisco (1999)Google Scholar
  7. 7.
    Joslin, D., Pollack, M.: Least-cost flaw repair: A plan refinement strategy for partial-order planning. In: Hayes-Roth, B., Korf, R. (eds.) Proc. of 12th National Conference on Artificial Intelligence (AAAI 1994), pp. 1004–1009. AAAI, Menlo Park (1994)Google Scholar
  8. 8.
    Gerevini, A., Schubert, L.: Accelerating partial-order planners: Some techniques for effective search control and pruning. Journal of Artificial Intelligence Research (JAIR) 5, 95–137 (1996)Google Scholar
  9. 9.
    Tate, A., Drabble, B., Kirby, R.: O-Plan2: An architecture for command, planning and control. In: Zweben, M., Fox, M. (eds.) Intelligent Scheduling, pp. 213–240. Morgan Kaufmann, San Francisco (1994)Google Scholar
  10. 10.
    Fukunaga, A., Rabideau, G., Chien, S., Yan, D.: Towards an application framework for automated planning and scheduling. In: Proc. of 1997 Int. Symp. on AI, Robotics & Automation for Space (1997)Google Scholar
  11. 11.
    PLANFORM: An open environment for building planners (2001), Project web site at http://scom.hud.ac.uk/planform/
  12. 12.
    Yang, Q., Fong, P., Kim, E.: Design patterns for planning systems. In: Simmons, R., Veloso, M., Smith, S. (eds.) Proc. of 4th International Conference on Artificial Intelligence Planning Systems (AIPS 1998) Workshop on Knowledge Engineering and Acquisition for Planning: Bridging Theory and Practice, pp. 104–112. AAAI, Menlo Park (1998)Google Scholar
  13. 13.
    Kambhampati, S., Mali, A., Srivastava, B.: Hybrid planning for partially hierarchical domains. In: Rich, C., Mostow, J. (eds.) Proc. of 15th National Conference on Artificial Intelligence (AAAI 1998), pp. 882–888. AAAI, Menlo Park (1998)Google Scholar
  14. 14.
    Schattenberg, B., Biundo, S.: On the identification and use of hierarchical resources in planning and scheduling. In: Ghallab, M., Hertzberg, J., Traverso, P. (eds.) Proc. of 6th International Conference on Artificial Intelligence Planning Systems (AIPS 2002), pp. 263–272. AAAI, Menlo Park (2002)Google Scholar
  15. 15.
    Biundo, S., Holzer, R., Schattenberg, B.: Dealing with continuous resources in AI planning. In: Proceedings of the 4th International Workshop on Planning and Scheduling for Space (IWPSS 2004), ESA-ESOC, Darmstadt, Germany, European Space Agency Publications Division, pp. 213–218 (2004)Google Scholar
  16. 16.
    Biundo, S., Holzer, R., Schattenberg, B.: Project planning under temporal uncertainty. In: Castillo, L., Borrajo, D., Salido, M.A., Oddi, A. (eds.) Planning, Scheduling, and Constraint Satisfaction: From Theory to Practice. Frontiers in Artificial Intelligence and Applications, vol. 117, pp. 189–198. IOS Press, Amsterdam (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Bernd Schattenberg
    • 1
  • Andreas Weigl
    • 1
  • Susanne Biundo
    • 1
  1. 1.Dept. of Artificial IntelligenceUniversity of UlmUlmGermany

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