Uncertainty-aware situational understanding

Name: Uncertainty-aware situational understanding
Author: Tomsett, R., Kaplan, L., Cerutti, F., Sullivan, P., Vente, D., Vilamala, M. R., Kimmig, A., Preece, A., Şensoy, Murat

İsim	Uncertainty-aware situational understanding
Yazar	Tomsett, R., Kaplan, L., Cerutti, F., Sullivan, P., Vente, D., Vilamala, M. R., Kimmig, A., Preece, A., Şensoy, Murat
Basım Tarihi:	2019
Basım Yeri	- SPIE
Konu	Uncertainty aware, Situational understanding
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-1-5106-2678-2
Kayıt Numarası	53554c42-e2a8-4ab7-8013-3e3bfbd040dd
Lokasyon	Computer Science
Tarih	2019
Notlar	United States Department of Defense US Army Research Laboratory (ARL) ; U.K. Ministry of Defence
Örnek Metin	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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Uncertainty-aware situational understanding

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

Basım Tarihi 2019

Basım Yeri - SPIE

Konu Uncertainty aware, Situational understanding

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-1-5106-2678-2

Kayıt Numarası 53554c42-e2a8-4ab7-8013-3e3bfbd040dd

Lokasyon Computer Science

Tarih 2019

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

Örnek Metin 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