Semantic Attachments for Domain-Independent Planning Systems

  • Christian Dornhege
  • Patrick Eyerich
  • Thomas Keller
  • Sebastian Trüg
  • Michael Brenner
  • Bernhard Nebel
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 76)


Solving real-world problems using symbolic planning often requires a simplified formulation of the original problem, since certain subproblems cannot be represented at all or only in a way leading to inefficiency. For example, manipulation planning may appear as a subproblem in a robotic planning context or a packing problem can be part of a logistics task. In this paper we propose an extension of PDDL for specifying semantic attachments. This allows the evaluation of grounded predicates as well as the change of fluents by externally specified functions. Furthermore, we describe a general schema of integrating semantic attachments into a forward-chaining planner and report on our experience of adding this extension to the planners FF and Temporal Fast Downward. Finally, we present some preliminary experiments using semantic attachments.


Packing Problem External Module Module Call Logistics Domain Callback Function 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bacchus, F., Kabanza, F.: Using temporal logics to express search control knowledge for planning. Artif. Intell. 116(1-2), 123–191 (2000)MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Bäckström, C., Nebel, B.: Complexity results for SAS +  planning. Computational Intelligence 11(4), 625–655 (1995)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Blum, A., Furst, M.: Fast planning through planning graph analysis. In: Proc. IJCAI, pp. 1636–1642 (1995)Google Scholar
  4. 4.
    Botea, A., Müller, M., Schaeffer, J.: Using abstraction for planning in sokoban. In: Proc. Computers and Games, Edmonton, Canada, pp. 360–375 (2003)Google Scholar
  5. 5.
    Cambon, S., Gravot, F., Alami, R.: A robot task planer that merges symbolic and geometric reasoning. In: Proc. ECAI, pp. 895–899. IOS Press (2004)Google Scholar
  6. 6.
    Eyerich, P., Mattmüller, R., Röger, G.: Using the context-enhanced additive heuristic for temporal and numeric planning. In: Proc. ICAPS, pp. 130–137 (2009)Google Scholar
  7. 7.
    Fox, M., Long, D.: Identifying and managing combinatorial optimisation subproblems in planning. In: Proc. IJCAI, pp. 445–452 (2001)Google Scholar
  8. 8.
    Fox, M., Long, D.: PDDL 2. 1: An extension to PDDL for expressing temporal planning domains. JAIR 20, 61–124 (2003)zbMATHGoogle Scholar
  9. 9.
    Helmert, M.: The Fast Downward planning system. JAIR 26, 191–246 (2006)zbMATHGoogle Scholar
  10. 10.
    Helmert, M.: Concise finite-domain representations for PDDL planning tasks. AIJ 173, 503–535 (2009)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Helmert, M., Geffner, H.: Unifying the causal graph and additive heuristics. In: Proc. ICAPS 2008, pp. 140–147 (2008)Google Scholar
  12. 12.
    Hoffmann, J., Nebel, B.: The FF planning system: Fast plan generation through heuristic search. JAIR 14, 253–302 (2001)zbMATHGoogle Scholar
  13. 13.
    Konolige, K., Nilsson, N.J.: Multiple-agent planning systems. In: AAAI, pp. 138–142 (1980)Google Scholar
  14. 14.
    Kvarnström, J., Doherty, P.: TALplanner: A temporal logic based forward chaining planner. Ann. Math. Artif. Intell. 30(1-4), 119–169 (2000)zbMATHCrossRefGoogle Scholar
  15. 15.
    Martello, S., Pisinger, D., Vigo, D.: The three-dimensional bin packing problem. Oper. Res. 48, 256–267 (2000)MathSciNetzbMATHCrossRefGoogle Scholar
  16. 16.
    Nau, D.S., Au, T.-C., Ilghami, O., Kuter, U., William Murdock, J., Wau, D., Yaman, F.: Shop2: An HTN planning system. JAIR 20, 379–404 (2003)zbMATHGoogle Scholar
  17. 17.
    Orkin, J.: Three states and a plan: The A.I. of F.E.A.R. In: Proc. Game Developers Conference, San Jose, California (2006)Google Scholar
  18. 18.
    Srivastava, B., Kambhampati, S.: Scaling Up Planning by Teasing Out Resource Scheduling. In: Biundo, S., Fox, M. (eds.) ECP 1999. LNCS, vol. 1809, pp. 172–186. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  19. 19.
    Weyhrauch, R.W.: Prolegomena to a theory of mechanized formal reasoning. Artif. Intell. 13(1-2), 133–170 (1980)MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Christian Dornhege
    • 1
  • Patrick Eyerich
    • 1
  • Thomas Keller
    • 1
  • Sebastian Trüg
    • 1
  • Michael Brenner
    • 1
  • Bernhard Nebel
    • 1
  1. 1.Institut für InformatikAlbert-Ludwigs-Universität FreiburgFreiburgGermany

Personalised recommendations