Achieving Conditional Plans Through the Use of Classical Planning Algorithms

  • Jony Teixeira de Melo
  • Carlos Roberto Lopes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4140)


In this work we concentrate on generating plans that take into account conditional actions. The main idea was to develop an algorithm that extended classical formalisms in a general way. By general way, we mean a flexible formalism that could use any algorithm for classical planning without any change. The result of our efforts was the development of a planner that we called METAPlan. In this paper we describe METAPlan and show some results of its performance.


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  1. 1.
    Russell, S., Norvig, P.: Artificial Intelligence – A Modern Approach, 2nd edn. Prentice Hall, Englewood Cliffs (2003)Google Scholar
  2. 2.
    Pryor, L., Collins, G.: Planning for Contingencies: A Decision-based Approach. Journal of Artificial Intelligence Research 4, 287–339 (1996)Google Scholar
  3. 3.
    Penberthy, J.S., Weld, D.: UCPOP: A sound, complete, partial order planner for ADL. In: Proceedings Third International Conference on Principles of Knowledge Representation and Reasoning, pp. 103–114 (1992)Google Scholar
  4. 4.
    Weld, D., Anderson, C., Smith, D.: Extending Graphplan to Handle Uncertainty and Sensing Actions, pp. 897–904. AAAI, Menlo Park (1998)Google Scholar
  5. 5.
    Blum, A., Furst, M.L.: Fast planning through planning graph analysis. In: Proceedings. IJCAI 1995, Montreal, Canadá (1995)Google Scholar
  6. 6.
    Peot, M., Smith, D.: Conditional nonlinear planning. In: Proc. of 1st Int. Conf. on AI Planning Systems (AIPS 1992), pp. 189–197 (1992)Google Scholar
  7. 7.
    Goldman, R.P., Boddy, M.S.: Conditional linear planning. In: Proc. Second Int’l Conf. on Artificial Intelligence Planning Systems, pp. 80–85 (1994)Google Scholar
  8. 8.
    Etzioni, O., Hanks, S., Weld, D., Draper, D., Lesh, N., Williamson, M.: An Approach to Planning with Incomplete Information. In: Proc. 3rd Int. Conf. on Principles of Knowledge Representation and Reasoning, pp. 115–125 (1992)Google Scholar
  9. 9.
    Fikes, R.E., Nilsson, N.J.: STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence 2, 189–208 (1971)MATHCrossRefGoogle Scholar
  10. 10.
    McDermott, D., et al.: PDDL - The Planning Domain Definition Language, Technical Report CVC TR-98-003 / DCS TR -1165, Yale Center for Communicational Vision and Control (1998)Google Scholar
  11. 11.
    WFMC - Workflow Management Coalition, 2004, The workflow reference model (June 2004),
  12. 12.
    Warren, D.H.D.: Generating Conditional Plans and Programs. In: Proceedings of the Summer Conference on Artificial Intelligence and Simulation on Behavior, pp. 344–354 (1976)Google Scholar
  13. 13.
    Bryce, D., Kambhampati, S.: Cost Sensitive Reachability Heuristics for Handling State Uncertainty. In: 21st Conference on Uncertainty in Artificial Intelligence. University of Edinburgh, Edinburgh, Scotland (2005)Google Scholar
  14. 14.
    Brafman, R., Hoffmann, J.: Conformant Planning via Heuristic Forward Search: A New Approach. In: Koenig, S., Zilberstein, S., Koehler, J. (eds.) Proceedings of the 14th International Conference on Automated Planning and Scheduling. ICAPS 2004, Whistler, Canada, pp. 355–364 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jony Teixeira de Melo
    • 1
  • Carlos Roberto Lopes
    • 1
  1. 1.Faculdade de ComputaçãoUniversidade Federal de Uberlândia (UFU)Uberlândia/MGBrazil

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