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Real-time dynamic partial order planning for memory reconstruction in autonomous virtual agents
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Inampudi, Bhuvana Chandra. Real-time dynamic partial order planning for memory reconstruction in autonomous virtual agents. Retrieved from https://doi.org/doi:10.7282/T3FR002M

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TitleReal-time dynamic partial order planning for memory reconstruction in autonomous virtual agents
NameInampudi, Bhuvana Chandra (author); Kapadia, Mubbasir (chair); Rutgers University; Graduate School - New Brunswick
Date Created2017
Other Date2017-01 (degree)
SubjectComputer Science, Video games--Design
Extent1 online resource (vi, 45 p. : ill.)
DescriptionThis paper introduces a novel approach to generate narratives from the memories of in-game agents. We propose an agent framework to accommodate memory and perception in virtual agents. This system extracts agent auto-biographic memories as multiple partial narratives, and generates possible complete narratives. We employ a novel narrative merging and extrapolation technique to generate unique complete narratives based on partial narratives of multiple agents. This is used to generate unique narratives for specific narrative constraints. To generate narratives for massive open-world scenes we introduce a novel algorithm that generates dynamic partial plans of large action spaces in real-time. These plans repairs as per the user actions, there by generating a unique narrative for every different user interaction. We conducted a comparative study of our dynamic planning technique with existing planning techniques. Also, we tested our agent framework for a complex environment to verify its robustness.
NoteM.S.
NoteIncludes bibliographical references
Noteby Bhuvana Chandra Inampudi
Genretheses, ETD graduate
Persistent URLhttps://doi.org/doi:10.7282/T3FR002M
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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