A Composite Self-organisation Mechanism in an Agent Network

  • Dayong Ye
  • Minjie Zhang
  • Quan Bai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6997)

Abstract

Self-organisation provides a suitable paradigm for developing autonomic web-based applications, e.g., e-commerce. Towards this end, in this paper, a composite self-organisation mechanism in an agent network is proposed. Based on self-organisation principles, this mechanism enables agents to dynamically adapt relations with other agents, i.e., change the underlying network structure, to achieve efficient task allocation. The proposed mechanism integrates a trust model to assist agents in reasoning with whom to adapt relations and employs a multi-agent Q-learning algorithm for agents to learn how to adapt relations. Moreover, in this mechanism, it is considered that the agents are connected by weighted relations, instead of crisp relations.

Keywords

Trust Model Task Allocation Candidate Selection Agent Network Intermediate Agent 
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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References

  1. 1.
    DeWolf, T., Holvoet, T.: Towards autonomic computing: agent-based modelling, dynamical systems analysis, and decentralised control. In: The First Intern. Workshop on Auton. Comput. Princip. and Archit., Banff, Canda, pp. 10–20 (2003)Google Scholar
  2. 2.
    Gomes, E.R., Kowalczyk, R.: Dynamic analysis of multiage nt q-learning with e-greedy exploration. In: ICML 2009, Montreal, Canada, pp. 369–376 (June 2009)Google Scholar
  3. 3.
    Griffiths, N., Luck, M.: Changing neighbours: Improving tag-based cooperation. In: AAMAS 2010, Toronto, Canada, pp. 249–256 (May 2010)Google Scholar
  4. 4.
    Hermoso, R., Billhardt, H., Ossowski, S.: Role evolution in open mas as an infor- mation source for turst. In: AAMAS 2010, Canada, pp. 217–224 (May 2010)Google Scholar
  5. 5.
    Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: A survey. Journal of Artificial Intelligence Research 4, 237–285 (1996)Google Scholar
  6. 6.
    Kota, R., Gibbins, N., Jennings, N.R.: Self-organising agent organisations. In: AAMAS 2009, Budapest, Hungary, pp. 797–804 (May 2009)Google Scholar
  7. 7.
    Mathieu, P., Routier, J.C., Secq, Y.: Principles for dynamic multi-agent organizations. In: Kuwabara, K., Lee, J. (eds.) PRIMA 2002. LNCS (LNAI), vol. 2413, pp. 109–122. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  8. 8.
    Serugendo, G.D.M., Gleizes, M.P., K., A.: Self-organization in multi-agent systems. The Knowl. Engin. Review 20(2), 165–189 (2005)CrossRefGoogle Scholar
  9. 9.
    Smarandache, F., Dezert, J.: Advances and Applications of DSmT for information Fusion. America Research (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Dayong Ye
    • 1
  • Minjie Zhang
    • 1
  • Quan Bai
    • 2
  1. 1.University of WollongongWollongongAustralia
  2. 2.Auckland University of TechnologyAucklandNew Zealand

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