Advertisement

Integrating Function Application in State-Based Planning

  • Ute Schmid
  • Marina Müller
  • Fritz Wysotzki
Conference paper
  • 443 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2479)

Abstract

We present an extension of state-based planning from traditional Strips to function application, allowing to express operator effects as updates. As proposed in PDDL, fluent variables are introduced and, consequently, predicates are defined over general terms. Preconditions of operators are characterized as variable binding constraints with standard preconditions as a special case of equality constraints. Operator effects can be expressed by ADD/DEL effects and additionally by updates of fluent variables. Mixing ADD/DEL effects and updates in an operator is allowed. Updating can involve the application of user-defined and built-in functions of the language in which the planner is realized. We present an operational semantics of the extended language. We will give a variety of example domains which can be dealt with in an uniform way: planning with resource variables, numerical problems such as water jug, functional variants of Tower of Hanoi and blocks-world, list sorting, and constraint-logic programming.

Keywords

Free Variable Function Symbol Function Application Resource Variable Relational Symbol 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bacchus, F., Kautz, H., Smith, D. E., Long, D., Geffner, H., & Koehler, J. (2000). AIPS-00 Planning Competition, Breckenridge, CO.Google Scholar
  2. Bibel, W. (1998). Let’s plan it deductively. Artificial Intelligence, 103(1–2), 183–208.zbMATHCrossRefMathSciNetGoogle Scholar
  3. Blum, A., & Furst, M. (1997). Fast planning through planning graph analysis. Artificial Intelligence, 90(1–2), 281–300.zbMATHCrossRefGoogle Scholar
  4. Bonet, B., & Geffner, H. (1998). Learning sorting and decision trees with POMDPs. In Proc. 15th international conf. on machine learning (pp. 73–81). Morgan Kaufmann.Google Scholar
  5. Bonet, B., & Geffner, H. (1999). Planning as heuristic search: New results. In Proc. European Conference on Planning (ECP-99), Durham, UK. Springer.Google Scholar
  6. Ehrig, H., & Mahr, B. (1985). Fundamentals of algebraic specification 1. Springer.Google Scholar
  7. Field, A. J., & Harrison, P. G. (1988). Functional progamming. Reading, MA: Addison-Wesley.Google Scholar
  8. Fox, M., & Long, D. (2001). PDDL2.1: An extension to PDDLfor expressing temporal planning domains. http://www.dur.ac.uk/d.p.long/competition.html.
  9. Frühwirth, T., & Abdennadher, S. (1997). Constraint-programming. Berlin: Springer.Google Scholar
  10. Geffner, H. (2000). Functional Strips: A more flexible language for planning and problem solving. In J. Minker (Ed.), Logic-based artificial intelligence. Dordrecht: Kluwer.Google Scholar
  11. Kautz, H., & Selman, B. (1996). Pushing the envelope: Planning, propositional logic and stochastic search. In Proc. 13th national conference on artificial intelligence and 8th innovative applications of artificial intelligence conference (pp. 1194–1201).Google Scholar
  12. Koehler, J. (1998). Planning under resource constraints. In H. Prade (Ed.), Proc. 13th European Conference on Artificial Intelligence (ECAI-98 (p. 489–493). Wiley.Google Scholar
  13. Koehler, J., Nebel, B., & Hoffmann, J. (1997). Extending planning graphs to an ADL subset. In Proc. European Conference on Planning (ECP-97) (p. 273–285). Springer. (extended version as Technical Report No. 88/1997, University Freiburg)Google Scholar
  14. Laborie, P., & Ghallab, M. (1995). Planning with sharable resource constraints. In Proc. of the 14th IJCAI (p. 1643–1649). Morgan Kaufmann.Google Scholar
  15. Manna, Z., & Waldinger, R. (1987). How to clear a block: a theory of plans. Journal of Automated Reasoning, 3(4), 343–378.zbMATHCrossRefMathSciNetGoogle Scholar
  16. McDermott, D. (1998). PDDL-the planning domain definition language. http://ftp.cs.yale.edu/pub/mcdermott.
  17. McDermott, D. (2000). The 1998 AI planning systems competition. AI Magazine, 21(2).Google Scholar
  18. Müller, M. (2000). Integration von Funktionsanwendungen beim zustandsbasierten Planen. diploma thesis, Dep. of Computer Science, TU Berlin.Google Scholar
  19. Pednault, E. P. D. (1987). Formulating multiagent, dynamic-world problems in the classical planning framework. In M. P. Georgeff & A. L. Lansky (Eds.), Proc. Workshop on Reasoning About Actions and Plans (pp. 47–82). Morgan Kaufmann.Google Scholar
  20. Pednault, E. P. D. (1994). ADL and the state-transition model of action. Journal of Logic and Computation, 4(5), 467–512.zbMATHCrossRefMathSciNetGoogle Scholar
  21. Schmid, U., & Wysotzki, F. (1998). Induction of recursive program schemes. In Proc. 10th European Conference on Machine Learning (ECML-98) (Vol. 1398, p. 214–225). Springer.Google Scholar
  22. Schmid, U., & Wysotzki, F. (2000). Applying inductive programm synthesis to macro learning. In Proc. 5th Int.. Conf. on Artificial Intelligence Planning and Scheduling (p. 371–378).Google Scholar
  23. Sterling, L., & Shapiro, E. (1986). The art of Prolog: Advanced programming techniques. MIT Press.Google Scholar
  24. Veloso, M., Carbonell, J., Pérez, M. A., Borrajo, D., Fink, E., & Blythe, J. (1995). Integrating planning and learning: The Prodigy architecture. Journal of Experimental and Theoretical Artificial Intelligence, 7(1), 81–120.zbMATHCrossRefGoogle Scholar
  25. Weld, D. (1994). An introduction to least commitment planning. AI Magazine, 15(4), 27–61.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Ute Schmid
    • 1
  • Marina Müller
    • 2
  • Fritz Wysotzki
    • 3
  1. 1.Department of Mathematics and Computer ScienceUniversity OsnabrückOsnabrückGermany
  2. 2.Xtradyne Technologies AGBerlinGermany
  3. 3.Department of Computer ScienceTechnical University BerlinBerlinGermany

Personalised recommendations