Modeling cooperating agents scenarios by deductive planning methods and logical fiberings

  • Jochen Pfalzgraf
  • Ute Cornelia Sigmund
  • Karel Stokkermans
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 958)


We describe a small but non-trivial 3-agent-robotics scenario by two different methods, viz. resource-oriented deductive planning and logical fiberings. The ultimate aim is to find a semantics for planning methods by means of fiberings. To this end, a comparison of the two methods is made and illustrated by the sample scenario, and the correspondences between the basic notions for both methods are clarified. The fiberings method is found to be useful in modeling communication and interaction between cooperating agents, thanks to the local/global distinction that is inherent to this framework. Possible extensions of the framework, like e.g. formulas dependent on space and/or time, are discussed.


Base Space Global Section Planning Approach Local Section Logical 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    J. Allen, J. Hendler, and A. Tate. Readings in Planning. Morgan Kaufmann, San Mateo, 1990.Google Scholar
  2. 2.
    W. Bibel. A Deductive Solution for Plan Generation. New Generation Computing, 4:115–132, 1986.Google Scholar
  3. 3.
    A.H. Bond and L. Gasser, editors. Readings in Distributed Artificial Intelligence. Morgan Kaufmann Publ., San Mateo, California, 1988.Google Scholar
  4. 4.
    S. Brüning, S. Hölldobler, J. Schneeberger, U. C. Sigmund, and M. Thielscher. Disjunction in Resource-Oriented Ddeductive Planning. Technical Report AIDA-94-03, Intellektik, Informatik, TH-Darmstadt, March 1994.Google Scholar
  5. 5.
    E. Csuhaj-Varju and J. Kelemen. On the power of cooperation: a regular representation of recursively enumerable languages. Theoretical Computer Science, 81:305–310, 1991.CrossRefGoogle Scholar
  6. 6.
    F. Dargam, J. Pfalzgraf, K. Stokkermans, and V. Stahl. Towards a toolkit for benchmarking scenarios in robot multi-tasking. Technical Report 91-45.0, RISC-Linz, J. Kepler University, Linz, Austria, Europe, 1991.Google Scholar
  7. 7.
    L. Gasser and M.N. Huhns, editors. Distributed Artificial Intelligence. Morgan Kaufmann Publ., San Mateo, and Pitman, London, 1989.Google Scholar
  8. 8.
    G. Gro\e, S. Hölldobler, J. Schneeberger, U. Sigmund, and M. Thielscher. Equational Logic Programming, Actions, and Change. In Proc. Joint International Conference and Symposium on Logic Programming JICSLP'92, 1992.Google Scholar
  9. 9.
    S. Hölldobler and J. Schneeberger. A New Deductive Approach to Planning. New Generation Computing, 8:225–244, 1990.Google Scholar
  10. 10.
    S. Hölldobler and M. Thielscher. Actions and Specificity. In D. Miller, editor, Proceedings of the International Logic Programming Symposium (ILPS), pages 164–180, Vancouver, October 1993. MIT Press.Google Scholar
  11. 11.
    J. Kelemen. Syntactical models of distributed cooperative systems. J. Expt. Theor. Artif. Intell., 3:1–10, 1991.Google Scholar
  12. 12.
    V. Lifschitz. Formal theories of action. In International Joint Conference on Artificial Intelligence, pages 966–972. Morgan Kaufmann Publishers, Inc., 1987.Google Scholar
  13. 13.
    M. Masseron, C. Tollu, and J. Vauzielles. Generating Plans in Linear Logic. In Foundations of Software Technology and Theoretical Computer Science, pages 63–75. Springer, volume 472 of LNCS, 1990.Google Scholar
  14. 14.
    J. McCarthy. Situations and Actions and Causal Laws. Stanford Artificial Intelligence Project, Memo 2, 1963.Google Scholar
  15. 15.
    J. McCarthy. Applications of circumscription to formalizing common-sense knowledge. Artificial Intelligence, 28:89–116, 1986.CrossRefGoogle Scholar
  16. 16.
    J. McCarthy and P. Hayes. Some philosophical problems from the standpoint of artificial intelligence. In B. Meltzer and D. Michie, editors, Machine Intelligence, vol. 4, pages 463–502. Edinburgh University Press, Edinburgh, 1969. Also published in: [1].Google Scholar
  17. 17.
    J. Pfalzgraf. On geometric and topological reasoning in robotics. Submitted to the Special Issue on Artificial Intelligence and Symbolic Mathematical Computation of the Annals of Mathematics and Artificial Intelligence.Google Scholar
  18. 18.
    J. Pfalzgraf. Logical fiberings and polycontextural systems. In Philippe Jorrand and Jozef Kelemen, editors, Proc. Fundamentals of Artificial Intelligence Research, LNCS 535 (subseries LNAI), pages 170–184, 1991.Google Scholar
  19. 19.
    J. Pfalzgraf and K. Stokkermans. Scenario construction continued and extended with a view to test and enhancement of reasoning methods. Technical Report 92-27, RISC-Linz, J. Kepler University, Linz, Austria, Europe, May 1992.Google Scholar
  20. 20.
    J. Pfalzgraf and K. Stokkermans. On robotics scenarios and modeling with fibered structures. In J. Pfalzgraf and D. Wang, editors, Springer Series Texts and Monographs in Symbolic Computation, Automated Practical Reasoning: Algebraic Approaches. Springer Verlag, 1994.Google Scholar
  21. 21.
    E. Shapiro and A. Takeuchi. Object oriented programming in Concurrent Prolog. New Generation Computing, 1:25–48, 1983.Google Scholar
  22. 22.
    U.C. Sigmund. LLP — Lineare Logische Programmierung. Master's thesis, Intellektik, Informatik, TH Darmstadt, 1992.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Jochen Pfalzgraf
    • 1
  • Ute Cornelia Sigmund
    • 2
  • Karel Stokkermans
    • 1
  1. 1.RISC-LinzAustria
  2. 2.TH DarmstadtGermany

Personalised recommendations