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Felt: A Simple Story Sifter

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Interactive Storytelling (ICIDS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11869))

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Abstract

Story sifting, also known as story recognition, has been identified as one of the major design challenges currently facing interactive emergent narrative. However, despite continued interest in emergent narrative approaches, there has been relatively little work in the area of story sifting to date, leaving it unclear how a story sifting system might best be implemented and what challenges are likely to be encountered in the course of implementing such a system. In this paper, we present Felt, a simple query language-based story sifter and rules-based simulation engine that aims to serve as a first step toward answering these questions. We describe Felt’s architecture, discuss several design case studies of interactive emergent narrative experiences that make use of Felt, reflect on what we have learned from working with Felt so far, and suggest directions for future work in the story sifting domain.

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Notes

  1. 1.

    As distinct from the natural language understanding term “story recognition”, which refers to the identification of embedded story content in natural language text.

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Acknowledgements

The authors would like to thank Megna Anand, Anish Kashyap, Daniel Man, and Akhil Vemuri for their assistance in testing, debugging, and authoring content for Felt and Diarytown.

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Authors and Affiliations

  1. UC Santa Cruz, Santa Cruz, CA, 95064, USA

    Max Kreminski, Melanie Dickinson & Noah Wardrip-Fruin

Authors
  1. Max Kreminski
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  2. Melanie Dickinson
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  3. Noah Wardrip-Fruin
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Corresponding author

Correspondence to Max Kreminski .

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Editors and Affiliations

  1. University of Utah, Salt Lake City, UT, USA

    Rogelio E. Cardona-Rivera

  2. Georgia Institute of Technology, Atlanta, GA, USA

    Anne Sullivan

  3. University of Utah, Salt Lake City, UT, USA

    R. Michael Young

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Kreminski, M., Dickinson, M., Wardrip-Fruin, N. (2019). Felt: A Simple Story Sifter. In: Cardona-Rivera, R., Sullivan, A., Young, R. (eds) Interactive Storytelling. ICIDS 2019. Lecture Notes in Computer Science(), vol 11869. Springer, Cham. https://doi.org/10.1007/978-3-030-33894-7_27

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  • DOI: https://doi.org/10.1007/978-3-030-33894-7_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33893-0

  • Online ISBN: 978-3-030-33894-7

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