An official website of the United States government
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.
more »
« less
Evolving Interactive Narrative Worlds
An interactive narrative is bound by the context of the world where its story takes place. However, most work in interactive narrative generation takes its story world design and mechanics as given, which abdicates a large part of story generation to an external world designer. In this paper, we close the story world design gap with an evolutionary search framework for generating interactive narrative worlds and mechanics. Our framework finds story world designs that accommodate multiple distinct player roles. We evaluate our system with an action agreement ratio analysis that shows worlds generated by our framework provide a greater number of in-role action opportunities compared to story worlds randomly sampled from the generative space.
more »
« less
- Award ID(s):
- 2303650
- PAR ID:
- 10649694
- Publisher / Repository:
- The AAAI Press
- Date Published:
- Journal Name:
- Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
- Volume:
- 19
- Issue:
- 1
- ISSN:
- 2326-909X
- Page Range / eLocation ID:
- 126 to 135
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
In strong story experience management problems, an automated storytelling agent balances player autonomy with narrative structure in the context of an interactive story game world. However, it is possible for the game world to get softlocked in states outside narrative structures specified by the game designer. These states are called dead-ends. In this paper, we revisit adversarial strong story experience management, a framing of the experience management problem that models interactive storytelling as an adversarial game where dead-ends are losses. This framing is adversarial against narrative softlocks, not necessarily the player. We present a novel agent based on adversarial search and deep reinforcement learning, which is trained to avoid dead-ends while preserving player autonomy. We compare our approach to a reactive, narrative plan-based mediation system on a test set of games compatible with current narrative planning techniques. We show that our adversarial architecture outperforms narrative mediation on a suite of dead-end metrics during game trace and breadth-first tests of state transition system exploration, using classical and intentional planning domains.more » « less
-
Planning-based narrative generation is effective at producing stories with a logically-sound flow of events, but it can be limiting due to the rigidity of its constraints and the high burden on the domain author to define story-world objects, initial states, and author and character goals. Giving the system the freedom to add objects and events to the story-world history arbitrarily can improve variety and reduce authorial burden, but risks leading to stories that seem jarringly contrived to the audience. I propose to use question-answering as the antidote to contrivance in a highly-generative interactive narrative system: By modeling the player's beliefs about the story world, inferring the implicit questions the player may be asking through their interactions, and answering those questions in a way consistent with the player's prior knowledge, a system could focus on creating cohesion in the ways that matter most to the player while accepting a degree of contrivance in the details that the player is likely to overlook.more » « less
-
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.more » « less
-
This paper presents a software library that enumerates the space of a state transition system specified by an action language, visualizes the states and action connections as a graph, and modifies the visualization based on underlying features determined through state and graph analysis. The library is intended as a tool for strong story interactive narrative design.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1609/aiide.v19i1.27508
