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Uncertainty-aware situational understanding

Title	Uncertainty-aware situational understanding
Author	Tomsett, R., Kaplan, L., Cerutti, F., Sullivan, P., Vente, D., Vilamala, M. R., Kimmig, A., Preece, A., Şensoy, Murat
Publication Date:	2019
Publication Place	- SPIE
Subject	Uncertainty aware, Situational understanding
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	978-1-5106-2678-2
Record ID	53554c42-e2a8-4ab7-8013-3e3bfbd040dd
Library Location	Computer Science
Date	2019
Notes	United States Department of Defense US Army Research Laboratory (ARL) ; U.K. Ministry of Defence
Sample Text	Situational understanding is impossible without causal reasoning and reasoning under and about uncertainty, i.e. prob-abilistic 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.
Editör	Pham, T.
DOI	10.1117/12.2519945
Cilt	11006

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Özyeğin University

Uncertainty-aware situational understanding

Author Tomsett, R., Kaplan, L., Cerutti, F., Sullivan, P., Vente, D., Vilamala, M. R., Kimmig, A., Preece, A., Şensoy, Murat

Publication Date 2019

Publication Place - SPIE

Subject Uncertainty aware, Situational understanding

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 978-1-5106-2678-2

Record ID 53554c42-e2a8-4ab7-8013-3e3bfbd040dd

Library Location Computer Science

Date 2019

Notes United States Department of Defense US Army Research Laboratory (ARL) ; U.K. Ministry of Defence

Sample Text Situational understanding is impossible without causal reasoning and reasoning under and about uncertainty, i.e. prob-abilistic 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.

Editör Pham, T.

DOI 10.1117/12.2519945

Cilt 11006