Abstract
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.
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References
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)
Case, K., Zweifel, P.: Linear Transport Theory. Addison-Wesley Publishing Company, Massachusetts (1967)
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)
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)
Jennings, N.: An agent-based approach for building complex software systems. In: Proceedings of the Communications of the ACM, pp. 35–41 (2001)
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)
Levy, H., Lessman, F.: Finite Difference Equations. Dover Publications, New York (1992)
Murray, J.: Mathematical Biology. Springer, New York (1989)
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)
Simon, H.: The Sciences of the Artificial. MIT Press, Cambridge (1969)
Vladimirov, V.S.: Equations of Mathematical Physics. Dekker, New York (1971)
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)
Xuan, P., Lesser, V.R.: Incorporating uncertainty in agent commitments. In: Agent Theories, Architectures, and Languages, pp. 57–70 (1999)
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Raja, A., Klibanov, M. (2009). A Distributed Numerical Approach for Managing Uncertainty in Large-Scale Multi-agent Systems. In: Barley, M., Mouratidis, H., Unruh, A., Spears, D., Scerri, P., Massacci, F. (eds) Safety and Security in Multiagent Systems. Lecture Notes in Computer Science(), vol 4324. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04879-1_6
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DOI: https://doi.org/10.1007/978-3-642-04879-1_6
Publisher Name: Springer, Berlin, Heidelberg
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