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Graphical Structure of Bayesian Networks by Eliciting Mental Models of Experts

  • Udai Kumar KudikyalaEmail author
  • Mounika Bugudapu
  • Manasa Jakkula
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 77)

Abstract

In knowledge-driven approaches to construct Bayesian networks (BN), a knowledge engineer consults with a domain expert to elicit and represent the graphical structure of a BN. The directed graph along with the node probabilities are then used for prediction or diagnosis. In this paper, we present a formal approach to learning the graphical structure of BN using domain expert(s). The proposed PFNetBN technique elicits and represents the mental model of an expert as a directed Pathfinder network. This technique uses the Target method to capture causal/influence relationships among the probability nodes from experts. It then generates a directed graph by applying the Pathfinder algorithm. Consensus Pathfinder network may be generated if multiple experts are involved. This technique generated graphs that are similar to some academic examples from the BN literature. This technique may save time in eliciting and constructing the graphical structure of a BNfrom experts.

Keywords

Bayesian network Pathfinder network Mental model Directed graph Domain expert Target method 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Udai Kumar Kudikyala
    • 1
    Email author
  • Mounika Bugudapu
    • 1
  • Manasa Jakkula
    • 1
  1. 1.Computer Science and Engineering DepartmentKeshav Memorial Institute of TechnologyNarayanguda, HyderabadIndia

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