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Computer Science > Artificial Intelligence

arXiv:2204.09672 (cs)
[Submitted on 31 Mar 2022 (v1), last revised 11 Oct 2022 (this
version, v2)]

Title:TropeTwist: Trope-based Narrative Structure Generation

Authors:Alberto Alvarez, Jose Font
Download a PDF of the paper titled TropeTwist: Trope-based Narrative
Structure Generation, by Alberto Alvarez and 1 other authors
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    Abstract: Games are complex, multi-faceted systems that share
    common elements and underlying narratives, such as the conflict
    between a hero and a big bad enemy or pursuing a goal that
    requires overcoming challenges. However, identifying and
    describing these elements together is non-trivial as they might
    differ in certain properties and how players might encounter the
    narratives. Likewise, generating narratives also pose
    difficulties when encoding, interpreting, and evaluating them. To
    address this, we present TropeTwist, a trope-based system that
    can describe narrative structures in games in a more abstract and
    generic level, allowing the definition of games' narrative
    structures and their generation using interconnected tropes,
    called narrative graphs. To demonstrate the system, we represent
    the narrative structure of three different games. We use
    MAP-Elites to generate and evaluate novel quality-diverse
    narrative graphs encoded as graph grammars, using these three
    hand-made narrative structures as targets. Both hand-made and
    generated narrative graphs are evaluated based on their coherence
    and interestingness, which are improved through evolution.

          8 pages, Accepted and to appear in Proceedings of the 13th
Comments: Workshop on Procedural Content Generation, at the
          Foundations of Digital Games (FDG), 2022
Subjects: Artificial Intelligence (cs.AI); Neural and Evolutionary
          Computing (cs.NE)
Cite as:  arXiv:2204.09672 [cs.AI]
          (or arXiv:2204.09672v2 [cs.AI] for this version)
          https://doi.org/10.48550/arXiv.2204.09672
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Alberto Alvarez [view email]
[v1] Thu, 31 Mar 2022 16:02:17 UTC (2,918 KB)
[v2] Tue, 11 Oct 2022 16:07:25 UTC (472 KB)
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