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

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Abstract

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.

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References

  1. Blum, A., Furst, M.L.: Fast planning through planning graph analysis. Artif. Intell. 90, 281–300 (1997)

    Article  MATH  Google Scholar 

  2. Bonet, B., Geffner, H.: Planning as heuristic search. Artif. Intell. 129 (1–2), 5–33 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  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. 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)

  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)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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)

  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)

    MATH  Google Scholar 

  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)

    MATH  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

  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)

  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)

  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)

  16. Gerevini, A., Serina, I.: Planning as propositional CSP: from Walksat to local search for action graphs. Constraints 8(4), 389–413 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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 http://cs-www.cs.yale.edu/homes/dvm/ (1998)

  19. Helmert, M.: The fast downward planning system. J. Artif. Intell. Res. (JAIR) 26, 191–246 (2006)

    Article  MATH  Google Scholar 

  20. Helmert, M., Do, M., Refanidis, I.: Deterministic part of the 6th International Planning Competition (IPC-2008), Deterministic Part. In: http://ipc.informatik.uni-freiburg.de/ (2008)

  21. Hoffmann, J., Edelkamp, S.: The deterministic part of IPC-4: an overview. J. Artif. Intell. Res. (JAIR) 24, 519–579 (2005)

    MATH  Google Scholar 

  22. Hoffmann, J., Nebel, B.: The FF planning system: fast plan generation through heuristic search. J. Artif. Intell. Res. (JAIR) 14, 253–302 (2001)

    MATH  Google Scholar 

  23. Long, D., Fox, M.: Efficient implementation of the plan graph in STAN. J. Artif. Intell. Res. (JAIR) 10, 87–115 (1999)

    MATH  Google Scholar 

  24. Long, D., Fox, M.: The 3rd international planning competition: results and analysis. J. Artif. Intell. Res. (JAIR) 20, 1–59 (2003)

    Article  MATH  Google Scholar 

  25. Nguyen, X., Kambhampati, S.: Reviving partial order planning. In: Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01) (2001)

  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)

  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. Richter, S., Helmert, M., Westphal, M.: Landmarks revisited. In: Proceedings of the Twenty-third National Conference on Artificial Intelligence (AAAI-08) (2008)

  29. Sidney, S., Castellan, N.J.: Nonparametric Statistics for the Behavioral Sciences. McGraw-Hill (1988)

  30. Simon, H.A.: Models of Man. John Wiley & Sons Inc., New York, USA (1957)

    MATH  Google Scholar 

  31. S. Thièbaux, Hoffmann, J., and Nebel, B.: In defense of PDDL axioms. Artif. Intell. 168, 38–69 (2005)

    Article  MATH  Google Scholar 

  32. Wilcoxon, F., Wilcox, R.A.: Some Rapid Approximate Statistical Procedures. American Cyanamid Co., Pearl River, N.Y. (1964)

    Google Scholar 

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Correspondence to Ivan Serina.

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This work is a revised and significantly extended version of a paper appearing in the Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI-2005) [13].

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Gerevini, A.E., Saetti, A. & Serina, I. Planning in domains with derived predicates through rule-action graphs and local search. Ann Math Artif Intell 62, 259–298 (2011). https://doi.org/10.1007/s10472-011-9240-3

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  • DOI: https://doi.org/10.1007/s10472-011-9240-3

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