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Select the Unexpected: A Statistical Heuristic for Story Sifting

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Interactive Storytelling (ICIDS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13762))

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Abstract

Story sifting techniques, which aim to excavate potentially compelling microstories from vast chronicles of storyworld events, present a promising solution to the challenges of interactive emergent narrative. However, current sifting techniques (which rely on large numbers of hand-specified story sifting patterns to identify compelling microstories) are limited by their inability to determine which of many sifting pattern matches are likely to be the most interesting to a human interactor. We present a higher-level story sifting heuristic that addresses this problem by identifying sifting pattern matches that are especially unlikely from a statistical perspective, and illustrate how this heuristic leads to the surfacing of more interesting microstories.

M. Dickinson—Independent

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Notes

  1. 1.

    https://github.com/mkremins/statistical-sifting.

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Acknowledgements

Max Kreminski conducted part of this research while in residence at Stochastic Labs.

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Authors and Affiliations

  1. Santa Clara University, Santa Clara, USA

    Max Kreminski

  2. Santa Cruz, USA

    Melanie Dickinson

  3. University of California, Santa Cruz, USA

    Max Kreminski, Noah Wardrip-Fruin & Michael Mateas

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  1. Max Kreminski
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  2. Melanie Dickinson
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  3. Noah Wardrip-Fruin
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  4. Michael Mateas
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Correspondence to Max Kreminski .

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Editors and Affiliations

  1. Amsterdam University of Applied Sciences, Amsterdam, The Netherlands

    Mirjam Vosmeer

  2. University of Skövde, Skövde, Sweden

    Lissa Holloway-Attaway

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Kreminski, M., Dickinson, M., Wardrip-Fruin, N., Mateas, M. (2022). Select the Unexpected: A Statistical Heuristic for Story Sifting. In: Vosmeer, M., Holloway-Attaway, L. (eds) Interactive Storytelling. ICIDS 2022. Lecture Notes in Computer Science, vol 13762. Springer, Cham. https://doi.org/10.1007/978-3-031-22298-6_18

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  • DOI: https://doi.org/10.1007/978-3-031-22298-6_18

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