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.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-642-04879-1_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04878-4
Online ISBN: 978-3-642-04879-1
eBook Packages: Computer ScienceComputer Science (R0)