StoryVerse: Towards Co-authoring Dynamic Plot with LLM-based Character Simulation via Narrative Planning
Summary
Paper digest
What problem does the paper attempt to solve? Is this a new problem?
The paper aims to address the challenge of balancing authorial intent with emergent behaviors in plot creation for games using Large Language Models (LLMs) to drive character simulation . This problem is not entirely new, as previous work has utilized LLMs for story generation, highlighting the limitations in preserving long-term dependency and coherence in generated plots . The paper proposes a novel plot creation workflow that mediates between a writer's intent and emergent behaviors from LLM-driven character simulation, introducing the concept of "abstract acts" to facilitate dynamic story generation .
What scientific hypothesis does this paper seek to validate?
This paper aims to validate a scientific hypothesis related to plot creation workflow in interactive narrative design using Large Language Models (LLMs) and character simulation. The hypothesis focuses on mediating between a writer's authorial intent and emergent behaviors from LLM-driven character simulation through an authorial structure called "abstract acts" . The paper seeks to demonstrate the feasibility and applications of this proposed workflow by showcasing a proof-of-concept system named StoryVerse, which integrates LLM-based character simulation and narrative planning to generate dynamic stories .
What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?
The paper "StoryVerse: Towards Co-authoring Dynamic Plot with LLM-based Character Simulation via Narrative Planning" proposes several innovative ideas, methods, and models in the field of narrative planning and character simulation for game environments :
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Plot Creation Workflow: The paper introduces a plot creation workflow that mediates between a writer's authorial intent and the emergent behaviors from LLM-driven character simulation. This workflow involves defining high-level plot outlines known as "abstract acts" that are later transformed into concrete character action sequences through an LLM-based narrative planning process .
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Abstract Acts: The concept of "abstract acts" serves as an authorial structure that enables co-creating dynamic plots between a writer and an LLM-based character simulation. These abstract acts allow for the creation of "living stories" that dynamically adapt to various game world states, influenced by the writer's intentions, character simulation results, and player actions .
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LLM-Based Narrative Planning: The paper presents an LLM-based narrative planning process that iteratively generates and reviews character action sequences using LLMs and a simulated game environment. This process aims to create immersive narrative experiences tightly coupled with story and game mechanics, showcasing the potential of leveraging LLMs for interactive storytelling .
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Character Simulation: The system integrates an LLM-based character simulator similar to AgentVerse, allowing characters' behaviors to be driven by LLMs. This approach enables the generation of dynamic stories that adapt to player actions and interactions with the game environment, enhancing the overall player experience .
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Interactive Storytelling: By combining narrative planning, character simulation, and player interactions, the proposed system aims to facilitate the co-creation of stories by authors, character simulations, and players. This approach represents a novel direction in interactive narrative design workflows, leveraging LLMs to enable immersive and engaging narrative experiences in game environments .
Overall, the paper introduces a comprehensive framework that bridges the gap between authorial intent and emergent behaviors in narrative generation, offering a promising approach to dynamic plot creation and character simulation in interactive storytelling contexts. The proposed system, StoryVerse, introduces several key characteristics and advantages compared to previous methods in narrative planning and character simulation for game environments, as detailed in the paper:
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Balancing Authorial Intent and Emergent Behaviors: StoryVerse presents a novel plot creation workflow that mediates between a writer's authorial intent and the emergent behaviors from LLM-driven character simulation. This approach allows for the co-creation of dynamic plots by the author, character simulation, and player actions asynchronously, ensuring a balance between predefined narrative structures and emergent story elements .
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Abstract Acts Structure: The system utilizes an authorial structure called "abstract acts" to define high-level plot outlines that are later transformed into concrete character action sequences through an LLM-based narrative planning process. This structure enables the creation of "living stories" that dynamically adapt to various game world states, influenced by the writer's intentions, character simulation results, and player interactions .
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LLM-Based Narrative Planning: StoryVerse employs an LLM-based narrative planning process that iteratively generates and reviews character action sequences using LLMs and a simulated game environment. This approach leverages the capabilities of LLMs to drive character behaviors and generate dynamic narratives that respond to player actions and interactions, enhancing the overall player experience .
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Flexible and Generalizable Representation: The use of LLMs in StoryVerse allows for a more flexible and generalizable representation of authorial constraints on the plot search space compared to traditional symbolic planning approaches. This reduces the need for extensive knowledge engineering expertise and offers a more adaptable framework for creating immersive narrative experiences in game environments .
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Player Modeling and Immersive Experience: The system simulates player intervention by updating world states based on player actions, enhancing player modeling and personalizing the narrative experience. By involving characters preferred by the player/protagonist in the story instantiation process, StoryVerse aims to create more immersive and engaging narratives that align with player preferences and character roles .
In conclusion, StoryVerse represents a significant advancement in interactive narrative design workflows by effectively combining authorial intent, character simulation, and player interactions through the innovative use of LLMs, abstract acts, and narrative planning processes. These characteristics offer a promising approach to dynamic plot creation and immersive storytelling experiences in game environments, setting it apart from traditional methods in the field .
Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?
Several related research works exist in the field of narrative planning and character simulation using Large Language Models (LLMs) for dynamic plot creation:
- Mark O Riedl and Robert Michael Young have conducted research on narrative planning, focusing on balancing plot and character .
- Chris Martens and Rogelio Cardona-Rivera have explored best practices for procedural narrative generation .
- Zihao Wang, Shaofei Cai, and their team have worked on interactive planning with Large Language Models for open-world multi-task agents .
- Stephen Ware, R Young, and Cory Siler have contributed to narrative planning systems supporting conflict and intention .
- Yi Wang, Qian Zhou, and David Ledo have presented the StoryVerse system, which mediates between a writer's authored content and emergent behaviors from character simulation .
Noteworthy researchers in this field include Mark O Riedl, Robert Michael Young, Chris Martens, Rogelio Cardona-Rivera, Zihao Wang, Shaofei Cai, Stephen Ware, Cory Siler, Yi Wang, Qian Zhou, and David Ledo.
The key to the solution mentioned in the paper involves using Large Language Models (LLMs) to drive character behaviors, allowing plots to naturally emerge from interactions between virtual characters and their environments. This approach eliminates the need for extensive knowledge engineering work and enables the creation of dynamic stories through a novel authorial structure called "abstract acts" . The system, StoryVerse, integrates LLM-based character simulation and narrative planning to co-create dynamic plots that adapt to various game world states, influenced by the writer's intent, character simulation results, and player actions .
How were the experiments in the paper designed?
The experiments in the paper were designed to demonstrate the plot creation workflow using two story domains: The Ant & Dove and The Ville. These story domains were chosen based on their previous use in narrative planning and generative agents research . The experiments showcased the system's potential by generating dynamic plots through a novel authorial structure called "abstract acts" and an LLM-based narrative planning process . The authors implemented a basic LLM-based character simulation component and a proxy for the game environment to demonstrate the feasibility of the proposed workflow . The experiments utilized the gpt-4-0125-preview LLM for all reported experiments and had a maximum number of narrative plan revisions set at 2 .
What is the dataset used for quantitative evaluation? Is the code open source?
The dataset used for quantitative evaluation in the context of the research on co-authoring dynamic plots with LLM-based character simulation via narrative planning is not explicitly mentioned. However, the study mentions the intention to conduct systematic evaluations in terms of standard quantitative metrics for story generation . Regarding the open-source status of the code, the information provided in the context does not specify whether the code used in the study is open source or publicly available. Therefore, it is recommended to refer directly to the authors or the publication for more details on the availability of the code .
Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.
The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The paper introduces a novel plot creation workflow that mediates between a writer's authorial intent and the emergent behaviors from LLM-driven character simulation through abstract acts and LLM-based narrative planning . The system, StoryVerse, showcases the feasibility and applications of this proposed workflow with examples in different stories and game environments . The experiments demonstrate the dynamic adaptation of stories to various game world states, influenced by the writer's intent, character simulation results, and player actions .
Furthermore, the paper discusses the challenges faced in utilizing LLMs for story generation, such as preserving long-term dependency and coherence . It suggests potential solutions to mitigate these challenges, including increasing the size of the LLM's context window, utilizing Retrieval-Augmented Generation, and adopting a hierarchical generation approach . These discussions indicate a thorough analysis of the experimental results and a critical reflection on the limitations of the approach.
Overall, the paper's experiments and results provide strong empirical evidence supporting the effectiveness of the proposed workflow in balancing authorial intent and emergent behaviors for game narratives. The system's ability to create "living stories" that dynamically adapt to different game world states, while involving the writer, character simulation, and player, underscores the successful verification of the scientific hypotheses put forth in the paper .
What are the contributions of this paper?
The paper "StoryVerse: Towards Co-authoring Dynamic Plot with LLM-based Character Simulation via Narrative Planning" presents several key contributions:
- Plot Creation Workflow: The paper introduces a plot creation workflow that bridges the gap between a writer's authorial intent and the emergent behaviors from LLM-driven character simulation. This workflow allows for the creation of "living stories" that dynamically adapt to various game world states, influenced by the writer's intentions, character simulation results, and player actions .
- Abstract Acts: The concept of "abstract acts" is introduced as an authorial structure that enables the co-creation of dynamic plots between a writer and an LLM-based character simulation. These abstract acts define high-level plot outlines that are later transformed into concrete character action sequences through an LLM-based narrative planning process .
- Proof-of-Concept System - StoryVerse: The paper showcases a proof-of-concept system called StoryVerse that demonstrates the feasibility and applications of the proposed workflow. StoryVerse integrates an LLM-based character simulator and a novel LLM-based narrative planner to create dynamic stories influenced by the writer, character simulation, and player interactions .
- Balancing Authorial Intent and Emergent Behaviors: The paper aims to balance authorial intent with emergent behaviors for game worlds with LLM-driven virtual characters. By leveraging LLMs for character behaviors, the system allows for narratives to emerge from interactions between characters and their environments, providing rich and immersive narrative experiences that adapt to player actions .
- Innovative Workflow: The proposed workflow leverages LLMs to enable immersive narrative experiences tightly coupled with story and game mechanics. It represents an early step towards novel interactive narrative design workflows that enhance narrative experiences in games .
What work can be continued in depth?
Further work in this area can focus on enhancing the quality of generated plots by addressing challenges related to long-term dependency and coherence in Large Language Models (LLMs) . One approach could involve exploring solutions like increasing the size of the LLM's context window, utilizing Retrieval-Augmented Generation, or adopting a hierarchical generation approach to mitigate these issues . Additionally, there is a need to consider potential delays in the act execution process during gameplay due to frequent calls to LLMs . This could involve optimizing the efficiency of LLM usage to ensure smooth narrative progression in interactive storytelling experiences.