Detail publikačního výsledku

Understanding and taxonomy of uncertainty in modeling, simulation, and risk profiling for border control automation

YANUSHKEVICH, S.; EASTWOOD, S.; DRAHANSKÝ, M.; SHMERKO, V.

Originální název

Understanding and taxonomy of uncertainty in modeling, simulation, and risk profiling for border control automation

Anglický název

Understanding and taxonomy of uncertainty in modeling, simulation, and risk profiling for border control automation

Druh

Článek WoS

Originální abstrakt

This paper addresses the problem of trust in Modeling and Simulation (M&S) technologies, and uncertainty in applications to homeland security. The key goal of this paper is an extension of the notion of trusted M&S techniques for traveler risk assessment in mass-transit applications such as e-borders. Theories of uncertainty suggest that different understandings of uncertainty result in different mechanisms of its reduction. We show that a taxonomy of uncertainty that is accepted in philosophical studies, as well as the NATO methodology of uncertainty assessment (known as the Admiralty Code), can be useful in M&S. This paper overviews various approaches to M&S and focuses on a framework that is based on multi-source fusion mechanisms using Dempster Shafer (DS) theory. The DS metric is useful for the development of simulators, recommender machines, and risk profilers when expert knowledge is given in an imprecise form. The difference between the Bayesian and DS metrics is introduced via a demonstrative experiment from the area of traveler risk assessment using a biometric-enabled watchlist.

Anglický abstrakt

This paper addresses the problem of trust in Modeling and Simulation (M&S) technologies, and uncertainty in applications to homeland security. The key goal of this paper is an extension of the notion of trusted M&S techniques for traveler risk assessment in mass-transit applications such as e-borders. Theories of uncertainty suggest that different understandings of uncertainty result in different mechanisms of its reduction. We show that a taxonomy of uncertainty that is accepted in philosophical studies, as well as the NATO methodology of uncertainty assessment (known as the Admiralty Code), can be useful in M&S. This paper overviews various approaches to M&S and focuses on a framework that is based on multi-source fusion mechanisms using Dempster Shafer (DS) theory. The DS metric is useful for the development of simulators, recommender machines, and risk profilers when expert knowledge is given in an imprecise form. The difference between the Bayesian and DS metrics is introduced via a demonstrative experiment from the area of traveler risk assessment using a biometric-enabled watchlist.

Klíčová slova

Uncertainty taxonomy, Admiralty Code, automated border control, Bayesian network, watchlist technology, Dempster-
Shafer metric

Klíčová slova v angličtině

Uncertainty taxonomy, Admiralty Code, automated border control, Bayesian network, watchlist technology, Dempster-
Shafer metric

Autoři

YANUSHKEVICH, S.; EASTWOOD, S.; DRAHANSKÝ, M.; SHMERKO, V.

Rok RIV

2017

Vydáno

30.07.2016

ISSN

1548-5129

Periodikum

Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS

Svazek

15

Číslo

1

Stát

Spojené státy americké

Strany od

95

Strany do

109

Strany počet

15

URL

http://dms.sagepub.com/content/early/2016/07/28/1548512916660637.full.pdf?ijkey=R9SCdz5xzrtfIce&keytype=finite

BibTex

@article{BUT130916,
  author="Svetlana {Yanushkevich} and Shawn {Eastwood} and Martin {Drahanský} and Vlad. {Shmerko}",
  title="Understanding and taxonomy of uncertainty in modeling, simulation, and risk profiling for border control automation",
  journal="Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS",
  year="2016",
  volume="15",
  number="1",
  pages="95--109",
  doi="10.1177/1548512916660637",
  issn="1548-5129",
  url="http://dms.sagepub.com/content/early/2016/07/28/1548512916660637.full.pdf?ijkey=R9SCdz5xzrtfIce&keytype=finite"
}