Share Email Print

Proceedings Paper

Uncertainty-aware situational understanding
Author(s): Richard Tomsett; Lance Kaplan; Federico Cerutti; Paul Sullivan; Daniel Vente; Marc Roig Vilamala; Angelika Kimmig; Alun Preece; Murat Şensoy
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Situational understanding is impossible without causal reasoning and reasoning under and about uncertainty, i.e. probabilistic reasoning and reasoning about the confidence in the uncertainty assessment. We therefore consider the case of subjective (uncertain) Bayesian networks. In previous work we notice that when observations are out of the ordinary, confidence decreases because the relevant training data, effective instantiations, to determine the probabilities for unobserved variables, on the basis of the observed variables, is significantly smaller than the size of the training data, the total number of instantiations. It is therefore of primary importance for the ultimate goal of situational understanding to be able to efficiently determine the reasoning paths that lead to low confidence whenever and wherever it occurs: this can guide specific data collection exercises to reduce such an uncertainty. We propose three methods to this end, and we evaluate them on the basis of a case-study developed in collaboration with professional intelligence analysts.

Paper Details

Date Published: 10 May 2019
PDF: 15 pages
Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 110060L (10 May 2019); doi: 10.1117/12.2519945
Show Author Affiliations
Richard Tomsett, IBM United Kingdom Ltd. (United Kingdom)
Lance Kaplan, U.S. Army Research Lab. (United States)
Federico Cerutti, Cardiff Univ. (United Kingdom)
Paul Sullivan, Intellpoint, Inc. (United States)
Daniel Vente, Cardiff Univ. (United Kingdom)
Marc Roig Vilamala, Cardiff Univ. (United Kingdom)
Angelika Kimmig, Cardiff Univ. (United Kingdom)
Alun Preece, Cardiff Univ. (United Kingdom)
Murat Şensoy, Ozyegin Univ. (Turkey)

Published in SPIE Proceedings Vol. 11006:
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications
Tien Pham, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?