Subjective Bayesian Networks and human-in-the-Loop situational understanding

Braines, David, Thomas, Anna, Kaplan, Lance, Sensoy, Murat, Backdash, Jonathan Z., Ivanovska, Magdalena, Preece, Alun and Cerutti, Federico 2017. Subjective Bayesian Networks and human-in-the-Loop situational understanding. Graph Structures for Knowledge Representation and Reasoning - 5th International Workshop, GKR 2017, Springer, Item availability restricted.

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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 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.

Item Type:	Book Section
Date Type:	Publication
Status:	In Press
Schools:	Computer Science & Informatics Crime and Security Research Institute (CSURI)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	Springer
Last Modified:	30 Jul 2018 21:00
URI:	http://orca.cf.ac.uk/id/eprint/107285

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