Model-based Planning in Physical domains using SetGraphs

  • Max Garagnani
Conference paper


This paper proposes a non-propositional representation framework for planning in physical domains. Physical planning problems involve identifying a correct sequence (plan) of object manipulations, transformations and spatial rearrangements to achieve an assigned goal . The problem of the ramification of action effects causes most current (propositional) planning languages to have inefficient encodings of physical domains. A simpler and more efficient representation is proposed, in which actions, goals and world state are modelled using ‘setGraphs’. A set Graph is an abstract data-structure able to capture implicitly the structural and topological constraints of a physical domain. Despite being model-based, the representation also allows the use of types and propositional furmulae to specify additional domain constraints. Experimental results obtained with a specific implementation of the representation indicate significant improvements in performance in all of the domains considered.


Physical Domain Action Schema Plan Execution Mobile Object Topological Constraint 
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.


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  1. [1).
    A. Cesta and A. Oddi. DDL.I: A formal description of a constraint representation language for physical domains. In M. Ghallab and A. Milani, editors, New Directions in AI Planning, pages 341–352. IOS Press (Amsterdam), 1996. (Proceedings of the 3rd European Workshop on Planning (EWSP95), Assisi, Italy, September 1995).Google Scholar
  2. [2).
    M. Fox and D. Long. PDDL2.I: An extension to PDDL for expressing temporal planning domains. Journal of Artificial Intell igence Research Special issue on the 3rd International Planning Competition, 2003. (forthcoming).Google Scholar
  3. [3]
    M. Garagnani and Y. Ding. Model-based planning for objectrearrangement problems. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS-03)-Workshop on PDDL, pages 49–58, 2003.Google Scholar
  4. [4]
    J.R. Hayes and H.A. Simon. Psychological differences among problem isomorphs. In N.J. Castellan, D.B. Pisoni, and G.R. Potts, editors, Cognitive theory. Erlbaum, 1977.Google Scholar
  5. [5]
    K. Kotovsky, J.R. Hayes, and H.A. Simon. Why Are Some Problems Hard? Evidence from Tower of Hanoi. Cognitive Psychology, 17:248–294, 1985.CrossRefGoogle Scholar
  6. [6]
    Z. Kulpa. Diagrammatic representation and reasoning. Machine GRAPHICS & VISION, 3(1/2):77–103, 1994.Google Scholar
  7. [7]
    J.H. Larkin and H.A. Simon. Why a diagram is (sometimes) worth ten thousands words. Cognitive Science, 11:65–99, 1987.CrossRefGoogle Scholar
  8. [8]
    D. Liu and T.L. McCluskey. The OCL Language Manual, Version 1.2. Technical report, Department of Computing and Mathematical Sciences, University of Huddersfield (UK), 2000.Google Scholar
  9. [9]
    D. Long and M. Fox. Automatic synthesis and use of generic types in planning. In S. Chien, S. Kambhampati, and C.A. Knoblock, editors, Proceedings of the 5th International Conference on AI Planning and Scheduling Systems (AIPS’00), pages 196–205, Breckenridge, CO, April 2000. AAAI Press.Google Scholar
  10. [10]
    K. Myers and K. Konolige. Reasoning with analogical representations. In B. Xebel, C. Rich, and W. Swartout, editors, Principles of Knowledge Representation and Reasoning: Proceedings of the Third International Conference (KR92), pages 189–200. Morgan Kaufmann Publishers Inc., San Mateo, CA, 1992.Google Scholar
  11. [11]
    E.P.D. Pednault. Synthesizing plans that contain actions with contextdependent effects. Computational Intelligence, 4(4):356–372, 1988.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2004

Authors and Affiliations

  • Max Garagnani
    • 1
  1. 1.Department of ComputingThe Open UniversityMilton KeynesUK

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