Advertisement

Compilation Schemes: A Theoretical Tool for Assessing the Expressive Power of Planning Formalisms

  • Bernhard Nebel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1701)

Abstract

The recent approaches of extending the graphplan algorithm to handle more expressive planning formalisms raise the question of what the formal meaning of “expressive power” is. We formalize the intuition that expressive power is a measure of how concisely planning domains and plans can be expressed in a particular formalism by introducing the notion of “compilation schemes” between planning formalisms. Using this notion, we analyze the expressive power of a large family of propositional planning formalisms and show, e.g., that Gazen and Knoblock’s approach to compiling conditional effects away is optimal.

Keywords

Expressive Power Propositional Variant Propositional Atom Planning Formalism Propositional Formula 
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. 1.
    C. R. Anderson, D. E. Smith, and D. S. Weld. Conditional effects in graphplan. In R. Simmons, M. Veloso, and S. Smith, eds., Proc. AIPS-98, p. 44–53. AAAI Press, Menlo Park, 1998.Google Scholar
  2. 2.
    C. Bäckström. Expressive equivalence of planning formalisms. Artificial Intelligence, 76(1-2):17–34, 1995.CrossRefGoogle Scholar
  3. 3.
    A. L. Blum and M. L. Furst. Fast planning through planning graph analysis. Artificial Intelligence, 90(1-2):279–298, 1997.CrossRefGoogle Scholar
  4. 4.
    T. Bylander. The computational complexity of propositional STRIPS planning. Artificial Intelligence, 69(1-2):165–204, 1994.zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    M. Cadoli and F. M. Donini. A survey on knowledge compilation. AI Communications, 10(3,4):137–150, 1997.Google Scholar
  6. 6.
    R. E. Fikes and N. Nilsson. STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2:189–208, 1971.zbMATHCrossRefGoogle Scholar
  7. 7.
    M. Furst, J. B. Saxe, and M. Sipser. Parity, circuits, and the polynomial-time hierarchy. Mathematical Systems Theory, 17(1):13–27, Apr. 1984.zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    M. R. Garey and D. S. Johnson. Computers and Intractability—A Guide to the Theory of NP-Completeness. Freeman, San Francisco, CA, 1979.zbMATHGoogle Scholar
  9. 9.
    B. C. Gazen and C. Knoblock. Combining the expressiveness of UCPOP with the efficiencyof Graphplan. In Steel and Alami [18], p. 221–233.Google Scholar
  10. 10.
    S. Kambhampati, E. Parker, and E. Lambrecht. Understanding and extending Graphplan. In Steel and Alami [18], p. 260–272.Google Scholar
  11. 11.
    H. A. Kautz and B. Selman. Forming concepts for fast inference. In Proc. AAAI-92, p. 786–793, San Jose, CA, July 1992. MIT Press.Google Scholar
  12. 12.
    J. Koehler, B. Nebel, J. Hoffmann, and Y. Dimopoulos. Extending planning graphs to an ADL subset. In Steel and Alami [18], p. 273-285.Google Scholar
  13. 13.
    V. Lifschitz. On the semantics of STRIPS. In M. P. Georgeff and A. Lansky, eds., Reasoning about Actions and Plans: Proceedings of the 1986 Workshop, p. 1–9, Timberline, OR, June 1986. Morgan Kaufmann.Google Scholar
  14. 14.
    B. Nebel. On the compilability and expressive power of propositional planning formalisms. Technical Report 101, Albert-Ludwigs-Universität, Institut für Informatik, Freiburg, Germany, June 1998.Google Scholar
  15. 15.
    B. Nebel. What is the expressive power of disjunctive preconditions? Technical Report 18, Albert-Ludwigs-Universität, Institut für Informatik, Freiburg, Germany, Mar. 1999.Google Scholar
  16. 16.
    C. H. Papadimitriou. Computational Complexity. Addison-Wesley, Reading, MA, 1994.Google Scholar
  17. 17.
    E. P. Pednault. ADL: Exploring the middle ground between STRIPS and the situation calculus. In R. Brachman, H. J. Levesque, and R. Reiter, eds., Proc. KR-89, p. 324–331, Toronto, ON, May 1989. Morgan Kaufmann.Google Scholar
  18. 18.
    S. Steel and R. Alami, editors. Proc. ECP’97, Toulouse, France, Sept. 1997. Springer-Verlag.Google Scholar
  19. 19.
    C. K. Yap. Some consequences of non-uniform conditions on uniform classes. Theoretical Computer Science, 26:287–300, 1983.zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Bernhard Nebel
    • 1
  1. 1.Institut für InformatikAlbert-Ludwigs-UniversitätFreiburgGermany

Personalised recommendations