Team situation awareness measure using semantic utility functions for supporting dynamic decision-making

Ma, J; Lu, J; Zhang, G

Team situation awareness measure using semantic utility functions for supporting dynamic decision-making

Ma, J Lu, J

Zhang, G

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Publication Type:: Journal Article
Citation:: Soft Computing, 2010, 14 (12), pp. 1305 - 1316
Issue Date:: 2010-01-01

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Field	Value	Language
dc.contributor.author	Ma, J	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.date.issued	2010-01-01	en_US
dc.identifier.citation	Soft Computing, 2010, 14 (12), pp. 1305 - 1316	en_US
dc.identifier.issn	1432-7643	en_US
dc.identifier.uri	http://hdl.handle.net/10453/13474
dc.description.abstract	Team decision-making is a remarkable feature in a complex dynamic decision environment, which can be supported by team situation awareness. In this paper, a team situation awareness measure (TSAM) method using a semantic utility function is proposed. The semantic utility function is used to clarify the semantics of qualitative information expressed in linguistic terms. The individual and team situation awareness are treated as linguistic possibility distributions on the potential decisions in a dynamic decision environment. In the TSAM method, team situation awareness is generated through reasoning and aggregating individual situation awareness based on a multi-level hierarchy mental model of the team. Individual and team mental models are composed of key drivers and significant variables. An illustrative example in telecoms customer churn prediction is given to explain the effectiveness and the main steps of the TSAM method. © 2009 Springer-Verlag.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0880739
dc.relation	http://purl.org/au-research/grants/arc/DP0559213
dc.relation.ispartof	Soft Computing	en_US
dc.relation.isbasedon	10.1007/s00500-009-0500-7	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Team situation awareness measure using semantic utility functions for supporting dynamic decision-making	en_US
dc.type	Journal Article
utslib.citation.volume	12	en_US
utslib.citation.volume	14	en_US
utslib.for	1702 Cognitive Sciences	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.issue	12	en_US
pubs.publication-status	Published	en_US
pubs.volume	14	en_US

Abstract:

Team decision-making is a remarkable feature in a complex dynamic decision environment, which can be supported by team situation awareness. In this paper, a team situation awareness measure (TSAM) method using a semantic utility function is proposed. The semantic utility function is used to clarify the semantics of qualitative information expressed in linguistic terms. The individual and team situation awareness are treated as linguistic possibility distributions on the potential decisions in a dynamic decision environment. In the TSAM method, team situation awareness is generated through reasoning and aggregating individual situation awareness based on a multi-level hierarchy mental model of the team. Individual and team mental models are composed of key drivers and significant variables. An illustrative example in telecoms customer churn prediction is given to explain the effectiveness and the main steps of the TSAM method. © 2009 Springer-Verlag.

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http://hdl.handle.net/10453/13474