Designing organized agents for cooperation with real time constraints

  • Michel Occello
  • Yves Demazeau
  • Christof Baeijs
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1456)


The aim of this paper is to present our approach for designing Multi-Agent Systems in the context of collective robotics and more generally in the context of real time distributed artificial intelligence applications. The paper presents an agent model (ASTRO) especially adapted to a real time context and shows how the cooperation can be achieved with this model by integrating external organizations and interactions. A design methodology is introduced to build agents using social knowledge (interaction and organization). A platform is presented including software development tools supporting the approach.


Reasoning Process Agent Model Local Goal Real Time Constraint Perception Capability 
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.


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  1. 1.
    R.C. Arkin and D. MacKenzie. AuRA: Principles and practice in review. Journal of Experimental and Theoretical Artificial Intelligence, 9(2), 1997.Google Scholar
  2. 2.
    C. Baeijs. Fonctionnalite emergente dans une societe d'agents autonomes: Etude des aspects organisationnels dans les systemes multi-agents reactifs. PhD Thesis (In French), Institut National Polytechnique de Grenoble, (To appear) 1998.Google Scholar
  3. 3.
    S. Bussmann and Y. Demazeau. An agent model combining reactive and cognitive capabilities. In Proceedings of IEEE International Conference on Intelligent Robots and Systems — IROS'94, Munchen, September 1994.Google Scholar
  4. 4.
    D.D. Corkill. Advanced Architectures: Concurrency and Parallelism. In V. Jagannathan, R. Dodhiawala, and L.S. Baum, editors, Blackboard Architectures and Applications, chapter 11, pages 77–83. Academic Press, 1989.Google Scholar
  5. 5.
    Y. Demazeau. From cognitive interactions to collective behaviour in agent-based systems. In Proceedings of 1st European Conference on Cognitive Science, Saint Malo, France, april 1995.Google Scholar
  6. 6.
    R. Dodhiawala, N.S. Sridharan, P. Raulefs, and C. Pickering. Real-Time AI systems: a Definition and an Architecture. In Proc. of the International Joint Conference on Artificial Intelligence — IJCAI 89, 1989.Google Scholar
  7. 7.
    I.A. Fergusson. Toward an architecture for adaptative, rational, mobile agents. In E. Werner and Y. Demazeau; editors, Decentralized A.I. North Holland, 1992.Google Scholar
  8. 8.
    E. Gat. Integrating reaction and planning in a heterogeneous asynchronous architecture for mobile robot navigation. SIGART Bulletin, 2, 1991.Google Scholar
  9. 9.
    B. Hayes-Roth. An architecture for adaptative intelligent systems. Artificial Intelligence, 72(1–2):pp. 329–365, january 1995.Google Scholar
  10. 10.
    F.F. Ingrand and V. Coutance. Real time reasoning using procedural reasoning. Technical Report TR 93104, LAAS, Toulouse, France, 1993.Google Scholar
  11. 11.
    D. Kinny, A. Rao, and M. Georgeff. A Methodology and Modelling Technique for Systems of BDI Age nts. In W. Van de Velde and J. Perram, editors, 7th Workshop on Modelling Autonomous Agents in a Multi-Agent World, MAAMAW'96, volume LNAI 1038, pages 56–71. Springer-Verlag, 1996.Google Scholar
  12. 12.
    Jacek Malec. A unified Approach to Intelligent Agency. In M. Woolridge and Jennings N., editors, Proceedings of ECAI-94 ATAL Workshop on Agent Theories, Architectures, and Languages, volume LNAI 890, pages 232–244, Amsterdam, The Netherlands, August 1995. Springer-Verlag.Google Scholar
  13. 13.
    D. Moffat and N.H. Frijda. Where there's Will there's an agent. In M. Woolridge and N. Jennings, editors, Proceedings of ECAI-94 ATAL Workshop on Agent Theories, Architectures, and Languages, volume LNAI 890, pages 245–260, Amsterdam, The Netherlands, August 1995. Springer-Verlag.Google Scholar
  14. 14.
    M. Occello. Distributed and parallel blackboards: application to dynamic systems control in robotics and computer musics. PhD Thesis Report (In French, University of Nice-Sophia Antipolis, january 1993.Google Scholar
  15. 15.
    M. Occello and Y Demazeau. Modelling decision making systems using agents satisfying real time constraints. In 3rd IFAC Symposium on Intelligent Autonomous Vehicles, Madrid, Spain, march 1998.Google Scholar
  16. 16.
    F. Scholastique. RESO: a tool to represent and exploit organizational knowledge. Master of Science (In French), CNAM march 1998.Google Scholar
  17. 17.
    J.S. Sichman, R. Conte, and Y. Demazeau. A social reasoning mechanism based on dependence networks. In Proceedings of ECAI'94-European Conference on Artificial Intelligence, Amsterdam, The Netherlands, August 1994.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Michel Occello
    • 1
  • Yves Demazeau
    • 1
  • Christof Baeijs
    • 1
  1. 1.LEIBNIZ/IMAG/CNRSGrenoble CedexFrance

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