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Theory of Influence Networks

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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.

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Authors and Affiliations

  1. George Mason University, Fairfax, VA, 22030, USA

    Abbas K. Zaidi & Faisal Mansoor

  2. University of Colorado, Denver, CO, 80270, USA

    Titsa P. Papantoni-Kazakos

Authors
  1. Abbas K. Zaidi
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  2. Faisal Mansoor
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  3. Titsa P. Papantoni-Kazakos
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Corresponding author

Correspondence to Titsa P. Papantoni-Kazakos.

Additional information

This work was supported by the Air Force Office of Scientific Research (AFOSR) under Grants FA9550-05-1-0106 and FA9550-05-1-0388.

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Zaidi, A.K., Mansoor, F. & Papantoni-Kazakos, T.P. Theory of Influence Networks. J Intell Robot Syst 60, 457–491 (2010). https://doi.org/10.1007/s10846-010-9425-8

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  • DOI: https://doi.org/10.1007/s10846-010-9425-8

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