Solving Non-deterministic Planning Problems with Pattern Database Heuristics

  • Pascal Bercher
  • Robert Mattmüller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5803)


Non-determinism arises naturally in many real-world applications of action planning. Strong plans for this type of problems can be found using AO* search guided by an appropriate heuristic function. Most domain-independent heuristics considered in this context so far are based on the idea of ignoring delete lists and do not properly take the non-determinism into account. Therefore, we investigate the applicability of pattern database (PDB) heuristics to non-deterministic planning. PDB heuristics have emerged as rather informative in a deterministic context. Our empirical results suggest that PDB heuristics can also perform reasonably well in non-deterministic planning. Additionally, we present a generalization of the pattern additivity criterion known from classical planning to the non-deterministic setting.


Heuristic search non-deterministic planning PDB heuristics 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cimatti, A., Pistore, M., Roveri, M., Traverso, P.: Weak, strong, and strong cyclic planning via symbolic model checking. Artificial Intelligence 147(1–2), 35–84 (2003)MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Edelkamp, S., Kissmann, P.: Solving fully-observable non-deterministic planning problems via translation into a general game. In: Proc. 32nd German Annual Conference on Artificial Intelligence, KI 2009 (2009)Google Scholar
  3. 3.
    Bryce, D., Kambhampati, S., Smith, D.E.: Planning graph heuristics for belief space search. Journal of Artificial Intelligence Research 26, 35–99 (2006)CrossRefMATHGoogle Scholar
  4. 4.
    Hoffmann, J., Brafman, R.I.: Contingent planning via heuristic forward search with implicit belief states. In: Proc. 15th International Conference on Automated Planning and Scheduling (ICAPS 2005), pp. 71–80 (2005)Google Scholar
  5. 5.
    Bercher, P., Mattmüller, R.: A planning graph heuristic for forward-chaining adversarial planning. In: Proc. 18th European Conference on Artificial Intelligence (ECAI 2008), pp. 921–922 (2008)Google Scholar
  6. 6.
    Hansen, E.A., Zilberstein, S.: LAO*: A heuristic search algorithm that finds solutions with loops. Artificial Intelligence 129(1–2), 35–62 (2001)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Nilsson, N.J.: Principles of Artificial Intelligence. Springer, Heidelberg (1980)MATHGoogle Scholar
  8. 8.
    Bercher, P.: Anwendung von Pattern-Database-Heuristiken zum Lösen nichtdeterministischer Planungsprobleme. Diplomarbeit, Albert-Ludwigs-Universität Freiburg im Breisgau (2009)Google Scholar
  9. 9.
    Edelkamp, S.: Planning with pattern databases. In: Proc. 6th European Conference on Planning (ECP 2001), pp. 13–24 (2001)Google Scholar
  10. 10.
    Edelkamp, S.: Automated creation of pattern database search heuristics. In: Edelkamp, S., Lomuscio, A. (eds.) MoChArt IV. LNCS (LNAI), vol. 4428, pp. 35–50. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Haslum, P., Botea, A., Bonet, B., Helmert, M., Koenig, S.: Domain-independent construction of pattern database heuristics for cost-optimal planning. In: Proc. 22nd AAAI Conference on Artificial Intelligence (AAAI 2007), pp. 1007–1012 (2007)Google Scholar
  12. 12.
    Hoffmann, J., Nebel, B.: The FF planning system: Fast plan generation through heuristic search. Journal of Artificial Intelligence Research 14, 253–302 (2001)MATHGoogle Scholar
  13. 13.
    Rintanen, J.: Constructing conditional plans by a theorem-prover. Journal of Artificial Intelligence Research 10, 323–352 (1999)MathSciNetMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Pascal Bercher
    • 1
  • Robert Mattmüller
    • 2
  1. 1.Institut für Künstliche IntelligenzUniversität UlmGermany
  2. 2.Institut für InformatikAlbert-Ludwigs-Universität FreiburgGermany

Personalised recommendations