System modeling of a smart-home healthy lifestyle assistant

Zhu, X; Yu, Y; Ou, Y; Luo, D; Zhang, C; Chen, J

System modeling of a smart-home healthy lifestyle assistant

Zhu, X Yu, Y Ou, Y Luo, D Zhang, C

Chen, J

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 7607 LNAI pp. 65 - 78
Issue Date:: 2013-02-04

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Field	Value	Language
dc.contributor.author	Zhu, X	en_US
dc.contributor.author	Yu, Y	en_US
dc.contributor.author	Ou, Y	en_US
dc.contributor.author	Luo, D	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.contributor.author	Chen, J	en_US
dc.date.issued	2013-02-04	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 7607 LNAI pp. 65 - 78	en_US
dc.identifier.isbn	9783642362873	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26589
dc.description.abstract	A system modeling is presented for a Smart-home Healthy Lifestyle Assistant System (SHLAS), covering healthy lifestyle promotion by intelligently collecting and analyzing context information, executing control instruction and suggesting health plans for users. SHLAS is Multi-agent based. Each agent has three levels: the Goal Layer has business rules for representing agent goals; the Strategy Layer provides technical rules and processes for guiding how the agent reacts to events; the Component Layer is made up of components, some components are called by technical rules and processes in the Strategy Layer, some others are used for communicating with third party systems. This agent framework enables the customizability of agents in SHLAS. We also introduce an Ontology-based domain knowledge and context model to capture and represent the agents, and agent behavior which provides agents with reasoning ability. SHLAS helps users with healthy lifestyle promotion by tracking and analyzing their behaviors, and recommending health plans. The paper closes with an empirical evaluation of the approach from the point of view of customizability. © 2013 Springer-Verlag.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-36288-0_7	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	System modeling of a smart-home healthy lifestyle assistant	en_US
dc.type	Conference Proceeding
utslib.citation.volume	7607 LNAI	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/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	7607 LNAI	en_US

Abstract:

A system modeling is presented for a Smart-home Healthy Lifestyle Assistant System (SHLAS), covering healthy lifestyle promotion by intelligently collecting and analyzing context information, executing control instruction and suggesting health plans for users. SHLAS is Multi-agent based. Each agent has three levels: the Goal Layer has business rules for representing agent goals; the Strategy Layer provides technical rules and processes for guiding how the agent reacts to events; the Component Layer is made up of components, some components are called by technical rules and processes in the Strategy Layer, some others are used for communicating with third party systems. This agent framework enables the customizability of agents in SHLAS. We also introduce an Ontology-based domain knowledge and context model to capture and represent the agents, and agent behavior which provides agents with reasoning ability. SHLAS helps users with healthy lifestyle promotion by tracking and analyzing their behaviors, and recommending health plans. The paper closes with an empirical evaluation of the approach from the point of view of customizability. © 2013 Springer-Verlag.

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http://hdl.handle.net/10453/26589