Engineering Adaptive Multi-Agent Systems with ODAM Methodology

  • Xinjun Mao
  • Jianming Zhao
  • Ji Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5044)


Agent orientation is believed as an appropriate and powerful paradigm to develop complex systems. In order to engineer complex self-adaptive multi-agent systems, we present dynamic binding mechanism and an agent-oriented methodology called ODAM that exploits the flexibility and high-level abstraction of agent orientation based on organization metaphors. The meta-model and modeling language of ODAM based on dynamic binding mechanism can effectively deal with the dynamic and self-adaptive aspects of MAS. Moreover, MDA approach and iteration development are integrated into ODAM to adapt to the variety of agent technologies and platforms, to deal with complexity of systems, and to simplify the development of MAS.


Modeling Language Model Transformation Multiagent System Agent Orientation Iteration Development 
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.
    Zambonelli, F., Van Dyke Parunak, H.: Towards a paradigm change in computer science and software engineering: a synthesis. The Knowledge Engineering Review 18(4), 329–342 (2003)CrossRefzbMATHGoogle Scholar
  2. 2.
    Mao, X., Yu, E.: Organizational and social concepts in agent oriented software engineering. In: Odell, J.J., Giorgini, P., Müller, J.P. (eds.) AOSE 2004. LNCS, vol. 3382, pp. 1–15. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    Luck, M., McBurney, P., Shehory, O., Willmott, S.: Agent Technology: Computing as Interaction (A Roadmap for Agent Based Computing), AgentLink (2005)Google Scholar
  4. 4.
    Cernuzzi, L., Zambonelli, F.: Dealing with adaptive multi-agent organizations in the gaia methodology. In: Müller, J.P., Zambonelli, F. (eds.) AOSE 2005. LNCS, vol. 3950, pp. 109–123. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Santos, D., Ribeiro, M.B., Bastos, R.M.: Developing a conference management system with the multi-agent systems unified process: A case study. In: Luck, M., Padgham, L. (eds.) Agent-Oriented Software Engineering VIII. LNCS, vol. 4951, pp. 212–224. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Ferber, J., Gutknecht, O.: A meta-model for the analysis and design of Organizations in MASs. In: Proc. Of ICMAS, pp. 128–135 (1998)Google Scholar
  7. 7.
    Zambonelli, F., Jennings, N.R., Wooldridge, M.: Developing Multiagent Systems: The Gaia Methodology. ACM Transactions on Software Engineering Methodology 12(3), 317–370 (2003)CrossRefGoogle Scholar
  8. 8.
    Juan, T., Sterling, L.: A Meta-model for Intelligent Adaptive MASs in Open Environments. In: Proc. Of AAMAS, pp. 1024–1025 (2003)Google Scholar
  9. 9.
    Odell, J.J., Nodine, M., Levy, R.: A metamodel for agents, roles, and groups. In: Odell, J.J., Giorgini, P., Müller, J.P. (eds.) AOSE 2004. LNCS, vol. 3382, pp. 78–92. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Giunchiglia, F., Mylopoulos, J., Perini, A.: The Tropos Development Methodology: Processes, Models and Diagrams. In: Proc. Of AAMAS, pp. 35–36 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Xinjun Mao
    • 1
  • Jianming Zhao
    • 2
  • Ji Wang
    • 1
  1. 1.Department of Computer ScienceNational Univ. of Defense TechnologyChangshaP.R. China
  2. 2.School of Computer ScienceZheJiang Normal Univ.JinghuaP.R. China

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