Subjective Bayesian Networks and Human-in-the-Loop Situational Understanding

  • Dave Braines
  • Anna Thomas
  • Lance Kaplan
  • Murat Şensoy
  • Jonathan Z. Bakdash
  • Magdalena Ivanovska
  • Alun Preece
  • Federico Cerutti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10775)


In this paper we present a methodology to exploit human-machine coalitions for situational understanding. Situational understanding refers to the ability to relate relevant information and form logical conclusions, as well as identify gaps in information. This process for comprehension of the meaning information requires the ability to reason inductively, for which we will exploit the machines’ ability to ‘learn’ from data. However, important phenomena are often rare in occurrence with high degrees of uncertainty, thus severely limiting the availability of instance data for training, and hence the applicability of many machine learning approaches. Therefore, we present the benefits of Subjective Bayesian Networks—i.e., Bayesian Networks with imprecise probabilities—for situational understanding, and the role of conversational interfaces for supporting decision makers in the evolution of situational understanding.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Dave Braines
    • 1
    • 2
  • Anna Thomas
    • 1
  • Lance Kaplan
    • 3
  • Murat Şensoy
    • 2
    • 6
  • Jonathan Z. Bakdash
    • 4
    • 5
  • Magdalena Ivanovska
    • 7
  • Alun Preece
    • 2
  • Federico Cerutti
    • 2
  1. 1.IBM Hursley ParkWinchesterUK
  2. 2.Cardiff UniversityCardiffUK
  3. 3.U.S. Army Research LaboratoryAdelphiUSA
  4. 4.U.S. Army Research Laboratory South Field ElementThe University of TexasDallasUSA
  5. 5.Texas A&M CommerceCommerceUSA
  6. 6.Ozyegin UniversityIstanbulTurkey
  7. 7.University of OsloOsloNorway

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