An Agent-Based Meta-level Architecture for Strategic Reasoning in Naval Planning

  • Mark Hoogendoorn
  • Catholijn M. Jonker
  • Peter-Paul van Maanen
  • Jan Treur
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3529)


The management of naval organizations aims at the maximization of mission success by means of monitoring, planning and strategic reasoning. This paper presents an agent-based meta-level architecture for the improvement of automated strategic reasoning in naval planning. The architecture is instantiated with decision knowledge acquired from naval domain experts and is formed into an executable agent-based model, which is used to perform a number of simulation runs. To evaluate the simulation results, relevant properties for the planning decision are identified and formalized. These important properties are validated for the simulation traces.


Meta-reasoning planning intelligent agent systems 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wilkins, D.E.: Domain-independent planning Representation and plan generation. Artificial Intelligence 22, 269–301 (1984)CrossRefGoogle Scholar
  2. 2.
    Maes, P., Nardi, D. (eds.): Meta-level architectures and reflection. Elsevier Science Publishers, Amsterdam (1988)zbMATHGoogle Scholar
  3. 3.
    Brazier, F.M.T., Dunin Keplicz, B., Jennings, N., Treur, J.: DESIRE: Modelling Multi-Agent Systems in a Compositional Formal Framework. International Journal of Cooperative Information Systems 6, 67–94 (1997)CrossRefGoogle Scholar
  4. 4.
    Shehory, O., Sturm, A.: Evaluation of modeling techniques for agent-based systems. In: Proceedings of the fifth international conference on Autonomous agents, Montreal, Canada, pp. 624–631 (2001)Google Scholar
  5. 5.
    Mulder, M., Treur, J., Fisher, M.: Agent Modelling in MetateM and DESIRE. In: Rao, A., Singh, M.P., Wooldridge, M.J. (eds.) ATAL 1997. LNCS (LNAI), vol. 1365, pp. 193–207. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  6. 6.
    Brazier, F.M.T., Jonker, C.M., Treur, J.: Compositional Design and Reuse of a Generic Agent Model. Applied Artificial Intelligence Journal 14, 491–538 (2000)CrossRefGoogle Scholar
  7. 7.
    Jonker, C.M., Treur, J.: A Compositional Process Control Model and its Application to Biochemical Processes. Applied Artificial Intelligence Journal 16, 51–71 (2002)CrossRefGoogle Scholar
  8. 8.
    Jonker, C.M., Treur, J.: Compositional verification of multi-agent systems: a formal analysis of pro-activeness and reactiveness. International. Journal of Cooperative Information Systems 11, 51–92 (2002)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Jonker, C.M., Treur, J., Wijngaards, W.C.A.: A Temporal Modelling Environment for Internally Grounded Beliefs, Desires and Intentions. Cognitive Systems Research Journal 4, 191–210 (2003)CrossRefGoogle Scholar
  10. 10.
    Bowen, K., Kowalski, R.: Amalgamating language and meta-language in logic program-ming. In: Clark, K., Tarnlund, S. (eds.) Logic programming. Academic Press, London (1982)Google Scholar
  11. 11.
    van der Hoek, W., Meyer, J.-J.Ch., Treur, J.: Formal Semantics of Meta-Level Architectures: Temporal Epistemic Reflection. International Journal of Intelligent Systems 18, 1293–1318 (2003)zbMATHCrossRefGoogle Scholar
  12. 12.
    Brazier, F.M.T., van Langen, P.H.G., Treur, J.: Strategic Knowledge in Design: a Compositional Approach. In: Hori, K. (ed.) Knowledge-based Systems, 11 Special Issue on Strategic Knowledge and Concept Formation, pp. 405–416 (1998)Google Scholar
  13. 13.
    Georgeff, M.P., Ingrand, F.F.: Decision-making in an embedded reasoning system. In: Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI-1989), Detroit, MI, pp. 972–978 (1989)Google Scholar
  14. 14.
    Sokolowski, J.: Enhanced Military Decision Modeling Using a MultiAgent System Approach. In: Proceedings of the Twelfth Conference on Behavior Representation in Modeling and Simulation, Scottsdale, AZ, May 12-15, pp. 179–186 (2003)Google Scholar
  15. 15.
    Pew, R.W., Mavor, A.S.: Modeling Human and Organizational Behavior. National Academy Press, Washington (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mark Hoogendoorn
    • 1
  • Catholijn M. Jonker
    • 3
  • Peter-Paul van Maanen
    • 1
    • 2
  • Jan Treur
    • 1
  1. 1.Dept. of Artificial IntelligenceVrije Universiteit AmsterdamAmsterdamThe Netherlands
  2. 2.Dept. of Information ProcessingTNO Human FactorsSoesterbergThe Netherlands
  3. 3.Nijmegen Institute for Cognition and InformationRadboud University NijmegenNijmegenThe Netherlands

Personalised recommendations