An approach for intent identification by building on deception detection

Judee Burgoon; Mark Adkins; John Kruse; Matthew L. Jensen; Thomas Meservy; Douglas P. Twitchell; Amit Deokar; Jay F. Nunamaker; Shan Lu; Gabriel Tsechpenakis; Dimitris N. Metaxas; Robert E. Younger

An approach for intent identification by building on deception detection

Judee Burgoon
, Mark Adkins
, John Kruse
, Matthew L. Jensen
, Thomas Meservy
, Douglas P. Twitchell
, Amit Deokar
, Jay F. Nunamaker
, Shan Lu
, Gabriel Tsechpenakis
, Dimitris N. Metaxas
, Robert E. Younger

Information Technology and Supply Chain Management Department

Research output: Contribution to journal › Conference article › peer-review

27 Scopus citations

Abstract

Past research in deception detection at the University of Arizona has guided the investigation of intent detection. A theoretical foundation and model for the analysis of intent detection is proposed. Available test beds for intent analysis are discussed and two proof-of-concept studies exploring nonverbal communication within the context of deception detection and intent analysis are shared.

Original language	English
Pages (from-to)	21
Number of pages	1
Journal	Proceedings of the Annual Hawaii International Conference on System Sciences
State	Published - 2005
Event	38th Annual Hawaii International Conference on System Sciences - Big Island, HI, United States Duration: 3 Jan 2005 → 6 Jan 2005

Cite this

@article{a90bbfd569b04ba780aeb854075ca985,

title = "An approach for intent identification by building on deception detection",

abstract = "Past research in deception detection at the University of Arizona has guided the investigation of intent detection. A theoretical foundation and model for the analysis of intent detection is proposed. Available test beds for intent analysis are discussed and two proof-of-concept studies exploring nonverbal communication within the context of deception detection and intent analysis are shared.",

author = "Judee Burgoon and Mark Adkins and John Kruse and Jensen, \{Matthew L.\} and Thomas Meservy and Twitchell, \{Douglas P.\} and Amit Deokar and Nunamaker, \{Jay F.\} and Shan Lu and Gabriel Tsechpenakis and Metaxas, \{Dimitris N.\} and Younger, \{Robert E.\}",

year = "2005",

language = "English",

pages = "21",

note = "38th Annual Hawaii International Conference on System Sciences ; Conference date: 03-01-2005 Through 06-01-2005",

}

TY - JOUR

T1 - An approach for intent identification by building on deception detection

AU - Burgoon, Judee

AU - Adkins, Mark

AU - Kruse, John

AU - Jensen, Matthew L.

AU - Meservy, Thomas

AU - Twitchell, Douglas P.

AU - Deokar, Amit

AU - Nunamaker, Jay F.

AU - Lu, Shan

AU - Tsechpenakis, Gabriel

AU - Metaxas, Dimitris N.

AU - Younger, Robert E.

PY - 2005

Y1 - 2005

N2 - Past research in deception detection at the University of Arizona has guided the investigation of intent detection. A theoretical foundation and model for the analysis of intent detection is proposed. Available test beds for intent analysis are discussed and two proof-of-concept studies exploring nonverbal communication within the context of deception detection and intent analysis are shared.

AB - Past research in deception detection at the University of Arizona has guided the investigation of intent detection. A theoretical foundation and model for the analysis of intent detection is proposed. Available test beds for intent analysis are discussed and two proof-of-concept studies exploring nonverbal communication within the context of deception detection and intent analysis are shared.

UR - https://www.scopus.com/pages/publications/27544473486

M3 - Conference article

AN - SCOPUS:27544473486

SN - 1530-1605

SP - 21

JO - Proceedings of the Annual Hawaii International Conference on System Sciences

JF - Proceedings of the Annual Hawaii International Conference on System Sciences

T2 - 38th Annual Hawaii International Conference on System Sciences

Y2 - 3 January 2005 through 6 January 2005

ER -

An approach for intent identification by building on deception detection

Abstract

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