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dc.contributor.advisorJulie A. Shah.en_US
dc.contributor.authorKim, Joseph, Ph..D. Massachusetts Institute of Technologyen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Aeronautics and Astronautics.en_US
dc.date.accessioned2015-09-17T19:05:11Z
dc.date.available2015-09-17T19:05:11Z
dc.date.copyright2015en_US
dc.date.issued2015en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/98691
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2015.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 61-66).en_US
dc.description.abstractUpon concluding a meeting, participants can occasionally leave with different understandings of what had been discussed. For meetings that result in immediate subsequent action, such as emergency response planning, all participants must share a common understanding of the decisions reached by the team in order to ensure successful execution of their mission. Thus, detecting inconsistencies in understanding among meeting participants is a desired capability for an intelligent system designed to monitor meetings and provide feedback to spur stronger shared understanding within a group. In this thesis, we present a computational model for the automatic prediction of consistency among team members' understanding of their group's decisions. The model utilizes dialogue features focused on capturing the dynamics of group decisionmaking. We trained our model using one of the largest publicly available meeting datasets and achieved a prediction accuracy rate of 64.2%, as well as robustness across different meeting phases. To the best of our knowledge, our work is the first to automatically predict levels of shared understanding using natural dialogue. We then implemented our model in an intelligent system that participated in human team planning meetings about a hypothetical emergency response mission. The system suggested discussion topics that the team would derive the most benefit from reviewing with one another. Through human subject experiments with 30 participants, we evaluated the utility of such a feedback system, and observed a statistically significant mean increase of 17.5% in objective measures of the consistency of the teams' understanding compared with that obtained using a baseline interactive system.en_US
dc.description.statementofresponsibilityby Joseph Kim.en_US
dc.format.extent66 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectAeronautics and Astronautics.en_US
dc.titleImproving team's consistency of understanding in meetings : intelligent agent participation and human subject studiesen_US
dc.title.alternativeImproving team's consistency of understanding : intelligent agent participation and human subject studiesen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Aeronautics and Astronauticsen_US
dc.identifier.oclc920687560en_US


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