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1. Intelligent De-Escalation Training via Emotion-Inspired Narrative Planning

   Fisher, Mira; Siler, Cory; Ware, Stephen G. (October 2022, Proceedings of the 13th workshop on Intelligent Narrative Technologies at the 18th AAAI conference on Artificial Intelligence and Interactive Digital Entertainment)

   McCoy, Josh; Treanor, Mike; Samuel, Ben (Ed.)

   We present an intelligent experience management architecture for a virtual reality police de-escalation training platform we are currently developing. Our aim is to direct the cast of non-player characters toward a scenario outcome appropriate to the player’s decisions, resulting in bad endings precisely when player’s mistakes enable them. We use a narrative planner to generate a story graph representing every possible narrative, and then we prune the graph to eliminate less believable non-player character actions. Unlike previous approaches based on story graph pruning, we implement an emotional planning model that lets us represent characters acting out of fear of bad outcomes as well as hope for good ones. We also incorporate experience management techniques for delaying commitment to hidden settings of the scenario and for capitalizing on player mistakes to demonstrate the negative consequences of not following best practices. 

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2. NarrativeGenie: Generating Narrative Beats and Dynamic Storytelling with Large Language Models

   https://doi.org/10.1609/aiide.v20i1.31868  

   Kumaran, Vikram; Rowe, Jonathan; Lester, James (November 2024, Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment)

   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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3. A Computational Model of Suspense for Non-Narrative Gameplay

   https://doi.org/10.1109/IV51561.2020.00136  

   Bailey, Evelyn; Zhu, Ying (September 2020, Proceedings of the 24th International Conference Information Visualisation)

   In recent years, various mechanisms have been proposed to optimize players’ emotional experience. In this paper, we focus on suspense, one of the key emotions in gameplay. Most previous research on suspense management in games focused on narratives. Instead, we propose a new computational model of Suspense for Non-Narrative Gameplay (SNNG). SNNG is built around a Player Suspense Model (PSM) with three key factors: hope, fear, and uncertainty. These three factors are modeled as three sensors that can be triggered by particular game objects (e.g., NPCs) and game mechanics (e.g., health). A player’s feeling of suspense can be adjusted by altering the level of hope, fear, and uncertainty. Therefore, an SNNG-enhanced game engine could manage a player’s level of suspense by adding or removing game objects, diverting NPCs, adjusting game mechanics, and giving or withholding information. We tested our model by integrating SNNG into a Pacman game. Our preliminary experiment with nine subjects was encouraging. 

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4. Evaluation of an Automatically-Constructed Graph-Based Representation for Interactive Narrative

   https://doi.org/10.1145/3337722.3341858  

   Partlan, N. (July 2020, Proceedings of the Foundations of Digital Games 2019)

   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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5. Solution Density and Search Strategy in Narrative Generation

   https://doi.org/10.1109/TG.2022.3149529  

   Siler, Cory; Ware, Stephen G. (January 2022, IEEE Transactions on Games)

   Intelligent interactive narrative systems coordinate a cast of non-player characters to make the overall story experience meaningful for the player. Narrative generation involves a tradeoff between plot-structure requirements and quality of character behavior, as well as computational efficiency. We study this tradeoff using the example of benchmark problems for narrative planning algorithms. A typical narrative planning problem calls for a sequence of actions that leads to an overall plot goal being met, while also requiring each action to respect constraints that create the appearance of character autonomy. We consider simplified solution definitions that enforce only plot requirements or only character requirements, and we measure how often each of these definitions leads to a solution that happens to meet both types of requirements—i.e., the density with which narrative plans occur among plot- or character-requirement-satisfying sequences. We then investigate whether solution densities can guide the selection of narrative planning algorithms. We compare the performance of two search strategies: one that satisfies plot requirements first and checks character requirements afterward, and one that continuously verifies character requirements. Our results show that comparing solution densities does not by itself predict which of these search strategies will be more efficient in terms of search nodes visited, suggesting that other important factors exist. We discuss what some of these factors could be. Our work opens further investigation into characterizing narrative planning algorithms and how they interact with specific domains. The results also highlight the diversity and difficulty of solving narrative planning problems. 

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