Designing Agents’ Behaviors and Interactions within the Framework of ADELFE Methodology

  • Carole Bernon
  • Valérie Camps
  • Marie-Pierre Gleizes
  • Gauthier Picard
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3071)


ADELFE is a methodology devoted to software engineering of adaptive multi-agent systems. Adaptive software is used in situations in which the environment is unpredictable or the system is open; in these cases designers cannot implement a global control on the system and cannot list all situations that the system has to be faced with. To solve this problem ADELFE guarantees that the software is developed according to the AMAS (Adaptive Multi-Agent System) theory2. This theory, based on self-organizing multi-agent systems, enables to build systems in which agents only pursue a local goal while trying to keep cooperative relations with other agents embedded in the system. ADELFE is linked with OpenTool, a commercialized graphical tool which supports UML notation. The paper focuses on the extension of OpenTool to take into account AMAS theory in designing agents’ behaviors. The modifications concern static aspects, by adding specific stereotypes, and dynamic aspects, with the automatic transformations from Agent Interaction Protocols into state machines. Then state machines simulate agent behaviors and enable testing and validating them.


Multiagent System Finite State Machine Cooperative Agent Designing Agent Cooperation Module 
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.
    Bernon, C., Gleizes, M.-P., Peyruqueou, S., Picard, G.: ADELFE, a Methodology for Adaptive Multi-Agent Systems Engineering. In: Petta, P., Tolksdorf, R., Zambonelli, F. (eds.) ESAW 2002. LNCS (LNAI), vol. 2577, pp. 156–169. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  2. 2.
    Brazier, F.M., Jonker, C.M., Treur, J.: Compositional design and reuse of a generic agent model. In: Proceeding of Knowledge Acquisition Workshop - KAW 1999 (1999)Google Scholar
  3. 3.
    Capera, D., Georgé, J.-P., Gleizes, M.-P., Glize, P.: The AMAS Theory for Complex Problem Solving Based on Self-organizing Cooperative Agents. In: 1st International Workshop on Theory And Practice of Open Computational Systems (TAPOCS 2003) at 12th IEEE International Workshops on Enabling Technologies (WETICE 2003), Infrastructure for Collaborative Enterprises, Linz, Austria, June 9-11, pp. 383–388. IEEE CS, Los Alamitos (2003)Google Scholar
  4. 4.
    Castro, J., Kolp, M., Mylopoulos, J.: A Requirements-driven Development Methodol-ogy. In: Dittrich, K.R., Geppert, A., Norrie, M.C. (eds.) CAiSE 2001. LNCS, vol. 2068, p. 108. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  5. 5.
    Cossentino, M.: Different Perspectives in Designing Multi-Agent System. In: AgeS 2002 (Agent Technology and Software Engineering) Workshop at NodE 2002, Erfurt, Germany (October 2002)Google Scholar
  6. 6.
    DeLoach, S.: Analysis and Design Using MaSE and agentTool. In: 12th Midwest A.I. and Cognitive Science Conference (MAICS 2001), Ohio (2001)Google Scholar
  7. 7.
    Desfray, P.: UML Profiles Versus Metamodel Extensions: An Ongoing Debate. In: OMG’s UML Workshops: UML in Enterprise: Modeling CORBA, Components, XML/XMI and Metadata Workshop (November 2000)Google Scholar
  8. 8.
    Eurescom, Project P907-GI - MESSAGE: Methodology for Engineering Systems of Software Agents, Deliverable 1 - Initial Methodology,
  9. 9.
    Georgé, J.-P., Gleizes, M.-P., Glize, P., Régis, C.: Real-time Simulation for Flood Forecast: an Adaptive Multi-Agent System STAFF. In: Proc. of the AISB 2003 symposium on Adaptive Agents and Multi-Agent Systems, Univ. of Wales, Aberystwyth (2003)Google Scholar
  10. 10.
    Georgé, J.-P., Picard, G., Gleizes, M.-P., Glize, P.: Living design for open computational systems. In: Fredriksson, M., Ricci, A., Gustavsson, R., Omicini, A. (eds.) International Workshop Theory And Practice of Open Computational Systems (TAPOCS) at 12th IEEE International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE 2003), Linz, Austria, June 2003, pp. 389–394. IEEE Computer Society, Los Alamitos (2003)Google Scholar
  11. 11.
    Gleizes, M.P., Camps, V., Glize, P.: A theory of emergent computation based on cooperative self-organization for adaptive artificial systems. In: Fourth European Congress on Systemic (1999), see also
  12. 12.
    Gleizes, M.-P., Glize, P., Link-Pezet, J.: An Adaptive Multi-Agent Tool For Electronic Commerce. In: The workshop on Knowledge Media Networking IEEE Ninth International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE 2000), Gaithersburg, Maryland, June 14-16 (2000)Google Scholar
  13. 13.
    Gleizes, M.-P., Millan, T., Picard, G.: ADELFE: Using SPEM Notation to Unify Agent Engineering Processes and Methodology, Rapport interne IRIT n° IRIT/2003-10-R (June 2003)Google Scholar
  14. 14.
    Giunchiglia, F., Mylopoulos, J., Perini, A.: The Tropos Software Development Methodology: Processes, Models and Diagrams. In: Giunchiglia, F., Odell, J.J., Weiss, G. (eds.) AOSE 2002. LNCS, vol. 2585, pp. 162–173. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  15. 15.
    Jacobson, I., Booch, G., Rumbaugh, J.: The Unified Software Development Process. Addison-Wesley, Reading (1999)Google Scholar
  16. 16.
    Lind, J.: Iterative Software Engineering for Multiagent Systems: the MASSIVE Method. LNCS (LNAI), vol. 1994. Springer, Heidelberg (2001)zbMATHCrossRefGoogle Scholar
  17. 17.
    Link-Pezet, J., Gleizes, M.-P., Glize, P.: FORSIC: a Self-Organizing Training System. In: International ICSC Symposium on Multi-Agents and Mobile Agents in Virtual Organizations and E-Commerce (MAMA 2000), Wollongong, Australia, December 11-13 (2000)Google Scholar
  18. 18.
    Müller, J.-P.: Emergence of collective behaviour and problem solving. In: Omicini, A., Petta, P., Pitt, J. (eds.) ESAW 2003. LNCS (LNAI), vol. 3071, pp. 1–20. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  19. 19.
    Odell, J., Parunak, H.V., Bauer, B.: Representing Agent Interaction Protocols in UML. In: Ciancarini, P., Wooldridge, M. (eds.) Agent Oriented Software Engineering, pp. 121–140. Springer, Berlin (2001)CrossRefGoogle Scholar
  20. 20.
    Odell, J., Parunak, H.V., Bauer, B.: Extending UML for Agents. In: Proceedings of the Agent Oriented Information Systems (AOIS) Workshop at the 17th National Conference on Artificial Intelligence, AAAI (2000)Google Scholar
  21. 21.
    OMG, Software Process Engineering Metamodel Specification,
  22. 22.
    Padgham, L., Winikoff, M.: Prometheus: A Pragmatic Methodology for Engineer-ing Intelligent Agents. In: Workshop on Agent-Oriented Methodologies at OOPSLA 2002 (2002)Google Scholar
  23. 23.
    Russel, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall Series (1995)Google Scholar
  24. 24.
    Wooldridge, M.: An introduction to multi-agent systems. John Wiley & Sons, Chichester (2000)Google Scholar
  25. 25.
    Wooldridge, M., Jennings, N.R., Kinny, D.: A Methodology for Agent-Oriented Analysis and Design. In: Proceedings of the 3rd International Conference on Autonomous Agents (Agents 1999), Seattle, WA, May 1999, pp. 69–76 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Carole Bernon
    • 1
  • Valérie Camps
    • 2
  • Marie-Pierre Gleizes
    • 1
  • Gauthier Picard
    • 1
  1. 1.IRIT, University Paul SabatierToulouse, Cedex 4France
  2. 2.L3I, University of La RochelleLa Rochelle, Cedex 1France

Personalised recommendations