Planning in domains with derived predicates through rule-action graphs and local search

  • Alfonso E. Gerevini
  • Alessandro Saetti
  • Ivan Serina


The ability to express derived predicates in the formalization of a planning domain is both practically and theoretically important. In this paper, we propose an approach to planning with derived predicates where the search space consists of “Rule-Action Graphs”, particular graphs of actions and rules representing derived predicates. We propose some techniques for representing such rules and reasoning with them, which are integrated into a framework for planning through local search and rule-action graphs. We also present some heuristics for guiding the search of a rule-action graph representing a valid plan. Finally, we analyze our approach through an extensive experimental study aimed at evaluating the importance of some specific techniques for the performance of the approach. The results of our experiments also show that our planner performs quite well compared to other state-of-the-art planners handling derived predicates.


Automated planning Domain-independent planning Efficient planning Planning with derived predicates  Heuristic search for planning 

Mathematics Subject Classifications (2010)

68T20 68T99 


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  1. 1.
    Blum, A., Furst, M.L.: Fast planning through planning graph analysis. Artif. Intell. 90, 281–300 (1997)MATHCrossRefGoogle Scholar
  2. 2.
    Bonet, B., Geffner, H.: Planning as heuristic search. Artif. Intell. 129 (1–2), 5–33 (2001)MathSciNetMATHCrossRefGoogle Scholar
  3. 3.
    Chen, Y., Hsu, C., Wah, B.: Temporal planning using subgoal partitioning and resolution in SGPlan. J. Artif. Intell. Res. (JAIR) 26, 323–369 (2006)Google Scholar
  4. 4.
    Davidson, M., Garagnani, M.: Pre-processing planning domains containing language axioms. In: Proceedings of the Twenty-first Workshop of the UK Planning and Scheduling SIG (PlanSIG-02) (2002)Google Scholar
  5. 5.
    Fox, M., Long, D.: PDDL2.1: An extension to PDDL for expressing temporal planning domains. J. Artif. Intell. Res. (JAIR) 20, 61–124 (2003)MATHGoogle Scholar
  6. 6.
    Friedman, M.: The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Am. Stat. Assoc. 32, 675–701 (1937)CrossRefGoogle Scholar
  7. 7.
    Garagnani, M.: A correct algorithm for efficient planning with preprocessed domain axioms. In: Research and Development in Intelligent Systems XVII, pp. 363–374. Springer-Verlag (2000)Google Scholar
  8. 8.
    Gazen, B., Knoblock, C.: Combining the expressivity of UCPOP with the efficiency of Graphplan. In: P roceedings of the Fourth European Conference on Planning (ECP-97) (1997)Google Scholar
  9. 9.
    Gerevini, A., Saetti, A., Serina, I.: Planning through stochastic local search and temporal action graphs. J. Artif. Intell. Res. (JAIR) 20, 239–290 (2003)MATHGoogle Scholar
  10. 10.
    Gerevini, A., Saetti, A., Serina, I.: An approach to temporal planning and scheduling in domains with predictable exogenous events. J. Artif. Intell. Res. (JAIR) 25, 187–231 (2006)MATHGoogle Scholar
  11. 11.
    Gerevini, A., Saetti, A., Serina, I.: An approach to efficient planning with numerical fluents and multi-criteria plan quality. Artif. Intell. 172(8–9), 899–944 (2008)MathSciNetMATHCrossRefGoogle Scholar
  12. 12.
    Gerevini, A., Saetti, A., Serina, I.: Planning in domains with derived predicates through rule-action graphs and local search. Technical Report R.T. 2010-04-64, Università degli Studi di Brescia, Dipartimento di Ingegneria dell’Informazione (2010)Google Scholar
  13. 13.
    Gerevini, A., Saetti, A., Serina, I., Toninelli, P.: Fast planning in domains with derived predicates: an approach based on rule-action graphs and local search. In: Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI-05) (2005)Google Scholar
  14. 14.
    Gerevini, A., Serina, I.: Fast planning through greedy action graphs. In: Proceedings of the Sixteenth National Conference of the American Association for Artificial Intelligence (AAAI-99) (1999)Google Scholar
  15. 15.
    Gerevini, A., Serina, I.: LPG: a planner based on local search for planning graphs with action costs. In: Proceedings of the Sixth International Conference on Artificial Intelligence Planning and Scheduling (AIPS-02) (2002)Google Scholar
  16. 16.
    Gerevini, A., Serina, I.: Planning as propositional CSP: from Walksat to local search for action graphs. Constraints 8(4), 389–413 (2003)MathSciNetMATHCrossRefGoogle Scholar
  17. 17.
    Gerevini, E., A., Haslum, P., Long, D., Saetti, A., Dimopoulos, Y.: Deterministic planning in the fifth international planning competition: PDDL3 and experimental evaluation of the planners. Artif. Intell. 173(5–6), 619–668 (2009)MathSciNetMATHCrossRefGoogle Scholar
  18. 18.
    Ghallab, M., Howe, A., Knoblock, C., McDermott, D., Ram, A., Veloso, M., Weld, D., Wilkins, D.: PDDL - the planning domain definition language. Technical Report CVC TR98-003/DCS TR-1165, Yale Center for Computational Vision and Control, available at (1998)
  19. 19.
    Helmert, M.: The fast downward planning system. J. Artif. Intell. Res. (JAIR) 26, 191–246 (2006)MATHCrossRefGoogle Scholar
  20. 20.
    Helmert, M., Do, M., Refanidis, I.: Deterministic part of the 6th International Planning Competition (IPC-2008), Deterministic Part. In: (2008)
  21. 21.
    Hoffmann, J., Edelkamp, S.: The deterministic part of IPC-4: an overview. J. Artif. Intell. Res. (JAIR) 24, 519–579 (2005)MATHGoogle Scholar
  22. 22.
    Hoffmann, J., Nebel, B.: The FF planning system: fast plan generation through heuristic search. J. Artif. Intell. Res. (JAIR) 14, 253–302 (2001)MATHGoogle Scholar
  23. 23.
    Long, D., Fox, M.: Efficient implementation of the plan graph in STAN. J. Artif. Intell. Res. (JAIR) 10, 87–115 (1999)MATHGoogle Scholar
  24. 24.
    Long, D., Fox, M.: The 3rd international planning competition: results and analysis. J. Artif. Intell. Res. (JAIR) 20, 1–59 (2003)MATHCrossRefGoogle Scholar
  25. 25.
    Nguyen, X., Kambhampati, S.: Reviving partial order planning. In: Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01) (2001)Google Scholar
  26. 26.
    Penberthy, J.S., Weld, D.S.: UCPOP: a sound, complete, partial order planner for ADL. In: Proceedings of the Third International Conference on Principles of Knowledge Representation and Reasoning (KR’92) (1992)Google Scholar
  27. 27.
    Pollack, M.E., Joslin, D., Paolucci, M.: Flaw selection strategies for partial-order planning. J. Artif. Intell. Res. (JAIR) 6, 223–262 (1997)Google Scholar
  28. 28.
    Richter, S., Helmert, M., Westphal, M.: Landmarks revisited. In: Proceedings of the Twenty-third National Conference on Artificial Intelligence (AAAI-08) (2008)Google Scholar
  29. 29.
    Sidney, S., Castellan, N.J.: Nonparametric Statistics for the Behavioral Sciences. McGraw-Hill (1988)Google Scholar
  30. 30.
    Simon, H.A.: Models of Man. John Wiley & Sons Inc., New York, USA (1957)MATHGoogle Scholar
  31. 31.
    S. Thièbaux, Hoffmann, J., and Nebel, B.: In defense of PDDL axioms. Artif. Intell. 168, 38–69 (2005)MATHCrossRefGoogle Scholar
  32. 32.
    Wilcoxon, F., Wilcox, R.A.: Some Rapid Approximate Statistical Procedures. American Cyanamid Co., Pearl River, N.Y. (1964)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Alfonso E. Gerevini
    • 1
  • Alessandro Saetti
    • 1
  • Ivan Serina
    • 2
  1. 1.Dipartimento di Ingegneria dell’InformazioneUniversità degli Studi di BresciaBresciaItaly
  2. 2.Free University of Bozen – BolzanoBressanoneItaly

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