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Interactive narrative in games utilize a combination of dynamic adaptability and predefined story elements to support player agency and enhance player engagement. However, crafting such narratives requires significant manual authoring and coding effort to translate scripts to playable game levels. Advances in pretrained large language models (LLMs) have introduced the opportunity to procedurally generate narratives. This paper presents NarrativeGenie, a framework to generate narrative beats as a cohesive, partially ordered sequence of events that shapes narrative progressions from brief natural language instructions. By leveraging LLMs for reasoning and generation, NarrativeGenie, translates a designer’s story overview into a partially ordered event graph to enable player-driven narrative beat sequencing. Our findings indicate that NarrativeGenie can provide an easy and effective way for designers to generate an interactive game episode with narrative events that align with the intended story arc while at the same time granting players agency in their game experience. We extend our framework to dynamically direct the narrative flow by adapting real-time narrative interactions based on the current game state and player actions. Results demonstrate that NarrativeGenie generates narratives that are coherent and aligned with the designer’s vision.
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Evaluation of an Automatically-Constructed Graph-Based Representation for Interactive Narrative
Interactivity and player experience are inextricably entwined with the creation of compelling narratives for interactive digital media. Narrative shapes and buttresses many such experiences, and therefore designers must construct compelling narrative arcs while carefully considering the effects of interaction on both the story and the player. As the narrative becomes more structurally complex, due to choice-based branching and other player actions, designers need to employ commensurately capable models and visualizations to keep track of that growing complexity. However, previous models of interactive narrative have failed to fully capture interactive elements with automated, operationalized visualizations. In this paper, we describe an algorithm for automated construction of a framework-driven, graph-based representation of interactive narrative. This representation more fully and transparently models structural and interactive features of the narrative than did prior approaches. We present an initial evaluation of this representation, based on modified cognitive walkthroughs performed by interactive narrative design and research experts from our research team, and we describe the takeaways for future improvement on interactive narrative modeling and analysis.
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- Award ID(s):
- 1736065
- PAR ID:
- 10191564
- Date Published:
- Journal Name:
- Proceedings of the Foundations of Digital Games 2019
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3337722.3341858
