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dc.contributor.advisorRiedl, Mark
dc.contributor.authorO'Neill, Brian
dc.date.accessioned2014-01-13T19:38:00Z
dc.date.available2014-01-13T19:38:00Z
dc.date.issued2013-11-18
dc.identifier.urihttp://hdl.handle.net/1853/50416
dc.description.abstractIn this dissertation, I present Dramatis, a computational human behavior model of suspense based on Gerrig and Bernardo's de nition of suspense. In this model, readers traverse a search space on behalf of the protagonist, searching for an escape from some oncoming negative outcome. As the quality or quantity of escapes available to the protagonist decreases, the level of suspense felt by the audience increases. The major components of Dramatis are a model of reader salience, used to determine what elements of the story are foregrounded in the reader's mind, and an algorithm for determining the escape plan that a reader would perceive to be the most likely to succeed for the protagonist. I evaluate my model by comparing its ratings of suspense to the self-reported suspense ratings of human readers. Additionally, I demonstrate that the components of the suspense model are sufficient to produce these human-comparable ratings.
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectArtificial intelligence
dc.subjectComputational creativity
dc.subjectStory generation
dc.subjectHuman behavior model
dc.subjectSuspense
dc.subject.lcshStorytelling
dc.subject.lcshArtificial intelligence Computer programs
dc.subject.lcshSuspense fiction
dc.titleA computational model of suspense for the augmentation of intelligent story generation
dc.typeDissertation
dc.description.degreePh.D.
dc.contributor.departmentInteractive Computing
dc.contributor.committeeMemberMagerko, Brian
dc.contributor.committeeMemberGoel, Ashok
dc.contributor.committeeMemberRam, Ashwin
dc.contributor.committeeMemberGervas, Pablo


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