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Article: Behavioral modeling with the new bio-inspired coordination generalized molecule model algorithm

TitleBehavioral modeling with the new bio-inspired coordination generalized molecule model algorithm
Authors
KeywordsCoordination Generalized Molecule Model (Cgmm)
Social Behavior
Social Coordination
Social Networks (Sn)
Issue Date2013
PublisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/ins
Citation
Information Sciences, 2013, v. 252, p. 1-19 How to Cite?
AbstractSocial Networks (SN) is an increasingly popular topic in artificial intelligence research. One of the key directions is to model and study the behaviors of social agents. In this paper, we propose a new computational model which can serve as a powerful tool for the analysis of SN. Specifically, we add to the traditional sociometric methods a novel analytical method in order to deal with social behaviors more effectively, and then present a new bio-inspired model, the coordination generalized molecule model (CGMM). The proposed analytical method for social behaviors and CGMM are combined to give an algorithm that can be used to solve complex problems in SN. Traditionally, SN models were mainly descriptive and were built at a very coarse level, typically with only a few global parameters, and turned out to be not sufficiently useful for analyzing social behaviors. In this work, we explore bio-inspired analytical models for analyzing social behaviors of intelligent agents. Our objective is to propose an effective and practical method to model intelligent systems and their behaviors in an open and complex unpredictable world. © 2011 Elsevier Inc. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/152488
ISSN
2015 Impact Factor: 3.364
2015 SCImago Journal Rankings: 2.513
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorFeng, Xen_US
dc.contributor.authorLau, FCMen_US
dc.contributor.authorYu, Hen_US
dc.date.accessioned2012-06-26T06:39:36Z-
dc.date.available2012-06-26T06:39:36Z-
dc.date.issued2013en_US
dc.identifier.citationInformation Sciences, 2013, v. 252, p. 1-19en_US
dc.identifier.issn0020-0255en_US
dc.identifier.urihttp://hdl.handle.net/10722/152488-
dc.description.abstractSocial Networks (SN) is an increasingly popular topic in artificial intelligence research. One of the key directions is to model and study the behaviors of social agents. In this paper, we propose a new computational model which can serve as a powerful tool for the analysis of SN. Specifically, we add to the traditional sociometric methods a novel analytical method in order to deal with social behaviors more effectively, and then present a new bio-inspired model, the coordination generalized molecule model (CGMM). The proposed analytical method for social behaviors and CGMM are combined to give an algorithm that can be used to solve complex problems in SN. Traditionally, SN models were mainly descriptive and were built at a very coarse level, typically with only a few global parameters, and turned out to be not sufficiently useful for analyzing social behaviors. In this work, we explore bio-inspired analytical models for analyzing social behaviors of intelligent agents. Our objective is to propose an effective and practical method to model intelligent systems and their behaviors in an open and complex unpredictable world. © 2011 Elsevier Inc. All rights reserved.en_US
dc.languageengen_US
dc.publisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/insen_US
dc.relation.ispartofInformation Sciencesen_US
dc.subjectCoordination Generalized Molecule Model (Cgmm)en_US
dc.subjectSocial Behavioren_US
dc.subjectSocial Coordinationen_US
dc.subjectSocial Networks (Sn)en_US
dc.titleBehavioral modeling with the new bio-inspired coordination generalized molecule model algorithmen_US
dc.typeArticleen_US
dc.identifier.emailLau, FCM:fcmlau@cs.hku.hken_US
dc.identifier.authorityLau, FCM=rp00221en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/j.ins.2011.12.003en_US
dc.identifier.scopuseid_2-s2.0-84884288181en_US
dc.identifier.isiWOS:000325674000001-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridFeng, X=55200149100en_US
dc.identifier.scopusauthoridLau, FCM=7102749723en_US
dc.identifier.scopusauthoridYu, H=7405854129en_US
dc.identifier.citeulike10165426-

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