Publication result detail

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

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

Original Title

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

English Title

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

Type

WoS Article

Original Abstract

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.

English abstract

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.

Keywords

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

Key words in English

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

Authors

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

RIV year

2017

Released

30.07.2016

ISBN

1548-5129

Periodical

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

Volume

15

Number

1

State

United States of America

Pages from

95

Pages to

109

Pages count

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"
}