Theory of Influence Networks

Authors:
Abbas K. Zaidi;Faisal Mansoor;Titsa P. Papantoni-Kazakos
Affiliations:
George Mason University, Fairfax, USA 22030;George Mason University, Fairfax, USA 22030;University of Colorado, Denver, USA 80270
Venue:
Journal of Intelligent and Robotic Systems
Year:
2010

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Abstract

Influence networks are Bayesian networks whose probabilities are approximated via expert provided influence constants. They represent a modeling and analysis formalism for addressing complex decision problems. In this paper, we present a comprehensive theory of influence networks that incorporates design constraints for consistency, temporal issues and a dynamic programming evolution of the influence constants. We also include numerical evaluations for several example timed influence networks.