Human-in-the-Loop Situational Understanding using Subjective Bayesian Networks: Experimental Results

Abstract 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 in- formation 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.
  • Dave Braines (IBM UK)
  • Anna Thomas (IBM UK)
  • Lance Kaplan (ARL)
  • Murat Sensoy
  • Jon Bakdash (ARL)
  • Magdalena Ivanovska
  • Alun Preece (Cardiff)
  • Federico Cerutti (Cardiff)
Date Sep-2018
Venue 2nd Annual Fall Meeting of the DAIS ITA, 2018