Sustainable Information Ecosystems

  • Rune Gustavsson
  • Martin Fredriksson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2603)


Fundamental challenges in engineering of large-scale multiagent systems involve qualitative requirements from, e.g., ambient intelligence and network-centric operations. We claim that we can meet these challenges if we model our multi-agent systems using models of evolutionary aspects of living systems. In current methodologies of multi-agent systems the notion of system evolution is only implicitly addressed, i.e., only closed patterns of interaction are considered as origin of dynamic system behaviour. In this paper we argue that service discovery and conjunction, by means of open patterns of interaction, are the basic tools for sustainable system behaviour. In effect, we introduce a framework for sustainable information ecosystems. Consequently, we describe basic principles of our methodology as well as a couple of applications illustrating our basic ideas. The applications coexist on our supporting agent society platform Solace and their respective behaviour is visualized using our system analysis tool Discern. The paper is concluded with a summary and a number of open research issues in the area.


Multiagent System Information Society Service Discovery Sustainability Invariant Lookup Service 
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.
    Blair, G., Coulson, G., Robin, and Papathomas, M. (1998) An Architecture for Next Generation Middleware. In Proceedings of Middleware’ 98. The Lake District, England.Google Scholar
  2. 2.
    Dertouzos, M. (1999). The future of computing. Scientific American.Google Scholar
  3. 3.
    Fredriksson, M. and Gustavsson, R. (2002) Theory and practice of behavior in open computational systems. In Müller, J. P. and Petta, P. (eds.) From agent theory to agent implementation. AT2AI3.Google Scholar
  4. 4.
    Gustavsson, R. and Fredriksson, M. (2002) Humans and complex systems: Sustainable information societies. In Olsson, M. O. and Sjöstedt, G. (eds.) Revealing complex structures: Challenges for Swedish systems analysis. FORMAS.Google Scholar
  5. 5.
    Gustavsson, R. and Fredriksson, M. (2001) Coordination and control of computational ecosystems: A vision of the future. In Omicini, A., Zambonelli, F., Klusch, M., and Tolksdorf, R. (eds.) Coordination of Internet agents: Models, technologies, and applications, pp. 443–469. Springer Verlag.Google Scholar
  6. 6.
    Highsmith III, J. A. (2000) Adaptive Software Development. In A Collaborative Approach to Managing Complex Systems. Dorset House Publishing Co., Dorset. ISBN 0-932633-40-4.Google Scholar
  7. 7.
    Iglesias, C., Garijo, M, and Gonzales, J. (1999). A Survey of Agent-Oriented Methodologies. In Intelligent Agents V-Proceedings of the Fifth International Workshop on Agent Theories, Architectures, and Languages (ATAL-98). Lecture Notes in Artificial Intelligence, Springer-Verlag, Heidelberg.Google Scholar
  8. 8.
    Jennings, N. R. (1999) Agent-based computing: Promise and perils. In Proceedings of Sixteenth international joint conference on Artificial intelligence, pp. 1429–1436.Google Scholar
  9. 9.
    Juan, T., Pearce, A., and Sterling, L. (2002). ROADMAP: Extending the Gaia Methodology for Complex Open Systems. In Proceedings of the First International Joint Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2002, ACM Press, 3–10.Google Scholar
  10. 10.
    Maturana, H. and Varela, F. (1980) Autopoesis and cognition. D. Reidel, Dortrecht, Holland, 1980.Google Scholar
  11. 11.
    Milnar, N., Gray, M., Roup, O., Kirkorian, R., and Maes, P. (1999). Hive: Distributed Agents for Networking Things. In Proceedings of ASA/MA’99.Google Scholar
  12. 12.
    Nardi, B. A. and O’Day, V. L. (1999) Information ecologies: Using technology with heart. The MIT Press. ISBN 0-262-14066-7.Google Scholar
  13. 13.
    Newell, A. (1982) The Knowledge level. In Artificial intelligence, no. 18, pp. 87–127.Google Scholar
  14. 14.
    Waldo, J. (1999). The Jini Architecture for Network-centric Computing. In Communications of the ACM, 76–82.Google Scholar
  15. 15.
    Wooldridge, M and Ciancarni, P. (2001). Agent-Oriented Software Engineering: The State of the Art. In Ciancarni, P and Wooldridge, M. (eds.) Agent-Oriented Software Engineering, Springer-Verlag Lecture Notes in AI, Volume 1957.Google Scholar
  16. 16.
    Wooldridge, M., Jennings, N., and Kinny; D. (2000) The Gaia Methodology for Agent-Oriented Analysis and Design. In Journal of Autonomous Agents and Multi-Agent Systems, 3(3), 25–312.CrossRefGoogle Scholar
  17. 17.
    Yokete, Y. (1992). The Apertos Re.ective Operating System: The Concept and its Implementation. In Proceedings of OOPSALA’92, ACM, 414–434.Google Scholar
  18. 18.
    Zambonelli, F., Jennings, N., Omnicini, A., and Wooldridge M. (2001) Agent-Oriented Software Engineering for Internet Applications. In A. Omicini, F. Zambonelli, M. Klusch, and R. Tolksdorf (eds.)Coordination of Internet Agents, Springer-Verlag, 326–346.Google Scholar
  19. 19.
    Zambonelli, F., Jennings, N., and Wooldridge, M. (2000) Organisational Abstractions for the Analysis and Design of Multi-Agent Systems. In Proceedings of First International Workshop on Agent-Oriented Software Engineering, Limerick, Ireland, 127–141.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Rune Gustavsson
    • 1
  • Martin Fredriksson
    • 1
  1. 1.Department of Software Engineering and Computer ScienceBlekinge Institute of TechnologyRonnebySweden

Personalised recommendations