Mathematical Analysis of Uncertainty Propagation in Agent Control

 

PIs: Anita Raja, Michael Klibanov (Math)

RAs: Niraj Mehta

Mathematical models of complex processes provide precise definitions of the processes and facilitate the prediction of process behavior for varying contexts. In this work, we study a numerical method for modeling the propagation of uncertainty in a multi-agent system (MAS) and a qualitative justification for this model. This model will 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.

Anita Raja and  Michael Klibanov, A Distributed Numerical Approach  for Managing Uncertainty in Large-Scale  Multi-Agent Systems To appear in LNAI Hot
Topics: Safety and  Security in Multiagent systems: The Early Years, pp: 75-84, volume 4324, editors: M. Barley, H. Mouratidis, A. Unruh , D. Spears, P. Scerri, F. Massacci, 2008.
 

M.V. Klibanov, Distributed modeling of propagation of computer viruses/worms by Partial Differential Equations, accepted for publication in Applicable Analysis, 2004.

Michael V. Klibanov and Alexandre Timonov Carleman Estimates for Coefficient Inverse Problems and Numerical Applications, Brill Academic Publishers, VSP (Imprint Brill) (Utrecht, Boston), 2004,