Subjective bayesian networks and human-in-the-loop situational understanding

Name: Subjective bayesian networks and human-in-the-loop situational understanding
Author: Braines, D., Thomas, A., Kaplan, L., Şensoy, Murat, Bakdash, J. Z., Ivanovska, M., Preece, A., Cerutti, F.

İsim	Subjective bayesian networks and human-in-the-loop situational understanding
Yazar	Braines, D., Thomas, A., Kaplan, L., Şensoy, Murat, Bakdash, J. Z., Ivanovska, M., Preece, A., Cerutti, F.
Basım Tarihi:	2018-03-21
Basım Yeri	- Springer
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-331978101-3
Kayıt Numarası	e2dc3a02-aadc-49a7-b2df-870c90b6a175
Lokasyon	Computer Science
Tarih	2018-03-21
Notlar	Army Research Laboratory ; Ministry of Defence
Örnek Metin	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.
DOI	10.1007/978-3-319-78102-0_2
Cilt	10775 LNAI

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Subjective bayesian networks and human-in-the-loop situational understanding

Yazar Braines, D., Thomas, A., Kaplan, L., Şensoy, Murat, Bakdash, J. Z., Ivanovska, M., Preece, A., Cerutti, F.

Basım Tarihi 2018-03-21

Basım Yeri - Springer

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-331978101-3

Kayıt Numarası e2dc3a02-aadc-49a7-b2df-870c90b6a175

Lokasyon Computer Science

Tarih 2018-03-21

Notlar Army Research Laboratory ; Ministry of Defence

Örnek Metin 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.

DOI 10.1007/978-3-319-78102-0_2

Cilt 10775 LNAI