Evolutionary learning of virtual team member preferences

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Virtual team members do not have a complete understanding of other team member (agent) preferences, which makes team coordination somewhat difficult. Traditional approaches for team coordination require a lot of inter-agent electronic communication and often result in wasted effort. Methods that reduce inter-agent communication and conflicts are likely to increase productivity of virtual teams. In this research, we propose an evolutionary genetic algorithm based intelligent agent that will learn team member preferences from past actions and develop an agent-coordination schedule by minimizing schedule conflicts between different members serving on a virtual team. Since the intelligent agent learns individual team member preferences, the potential for conflict is greatly reduced, which in turn results in lower inter-agent communication cost and increased team productivity.

Original languageEnglish (US)
Title of host publication2008 IEEE International on Professional Communication Conference, IPCC
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781424420865
StatePublished - 2008
Event2008 IEEE International Professional Communication Conference, IPCC 2008 - Montreal, QC, Canada
Duration: Jul 13 2008Jul 16 2008

Publication series

NameIEEE International Professional Communication Conference
ISSN (Print)2158-091X
ISSN (Electronic)2158-1002


Conference2008 IEEE International Professional Communication Conference, IPCC 2008
CityMontreal, QC

All Science Journal Classification (ASJC) codes

  • Communication
  • Engineering(all)


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