A Distributed Numerical Approach for Managing Uncertainty in Large-Scale Multi-agent Systems

  • Anita Raja
  • Michael Klibanov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4324)


Mathematical models of complex processes provide precise definitions of the processes and facilitate the prediction of process behavior for varying contexts. In this paper, we present a numerical method for modeling the propagation of uncertainty in a multi-agent system (MAS) and a qualitative justification for this model. We discuss how this model could help determine the effect of various types of uncertainty on different parts of the multi-agent system; facilitate the development of distributed policies for containing the uncertainty propagation to local nodes; and estimate the resource usage for such policies.


Continuous Model Small Time Interval Uncertainty Propagation Computer Virus Primitive Action 
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  1. 1.
    Boutilier, C., Dean, T., Hanks, S.: Planning under uncertainty: Structural assumptions and computational leverage. In: Ghallab, M., Milani, A. (eds.) New Directions in AI Planning, pp. 157–172. IOS Press, Amsterdam (1996)Google Scholar
  2. 2.
    Case, K., Zweifel, P.: Linear Transport Theory. Addison-Wesley Publishing Company, Massachusetts (1967)zbMATHGoogle Scholar
  3. 3.
    Dean, T., Kaelbling, L.P., Kirman, J., Nicholson, A.: Planning with deadlines in stochastic domains. In: Fikes, R., Lehnert, W. (eds.) Proceedings of the Eleventh National Conference on Artificial Intelligence, pp. 574–579. AAAI Press, Menlo Park (1993)Google Scholar
  4. 4.
    Decker, K.S., Lesser, V.R.: Quantitative modeling of complex computational task environments. In: Proceedings of the Eleventh National Conference on Artificial Intelligence, Washington, pp. 217–224 (1993)Google Scholar
  5. 5.
    Jennings, N.: An agent-based approach for building complex software systems. In: Proceedings of the Communications of the ACM, pp. 35–41 (2001)Google Scholar
  6. 6.
    Klibanov, M.: Distributed modeling of propagation of computer viruses/worms by partial differential equations. In: Proceedings of Applicable Analysis, Taylor and Francis Limited, September 2006, pp. 1025–1044 (2006)Google Scholar
  7. 7.
    Levy, H., Lessman, F.: Finite Difference Equations. Dover Publications, New York (1992)zbMATHGoogle Scholar
  8. 8.
    Murray, J.: Mathematical Biology. Springer, New York (1989)CrossRefzbMATHGoogle Scholar
  9. 9.
    Raja, A., Wagner, T., Lesser, V.: Reasoning about Uncertainty in Design-to-Criteria Scheduling. In: Working Notes of the AAAI 2000 Spring Symposium on Real-Time Systems, Stanford (2000)Google Scholar
  10. 10.
    Simon, H.: The Sciences of the Artificial. MIT Press, Cambridge (1969)Google Scholar
  11. 11.
    Vladimirov, V.S.: Equations of Mathematical Physics. Dekker, New York (1971)zbMATHGoogle Scholar
  12. 12.
    Wagner, T., Raja, A., Lesser, V.: Modeling uncertainty and its implications to design-to-criteria scheduling. Autonomous Agents and Multi-Agent Systems 13, 235–292 (2006)CrossRefGoogle Scholar
  13. 13.
    Xuan, P., Lesser, V.R.: Incorporating uncertainty in agent commitments. In: Agent Theories, Architectures, and Languages, pp. 57–70 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Anita Raja
    • 1
  • Michael Klibanov
    • 2
  1. 1.Department of Software and Information SystemsThe University of North Carolina at CharlotteCharlotte
  2. 2.Department of MathematicsThe University of North Carolina at CharlotteCharlotte

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