Procedural Artificial Narrative Generation using Large Language Models for Turn-Based Video Games

Conceitos essenciais

PANGeA, a structured approach for leveraging large language models to generate narrative content and foster dynamic, free-form interactions between players and the game environment in turn-based role-playing video games.

Resumo

The research introduces PANGeA (Procedural Artificial Narrative using Generative AI), a structured approach for leveraging large language models (LLMs) to generate narrative content and foster dynamic, free-form interactions between players and the game environment in turn-based role-playing video games (RPGs).

During game initialization, PANGeA uses a multi-step prompting sequence to generate baseline narrative assets, including the game setting, player persona, non-playable characters (NPCs), and narrative beats. The NPCs are assigned personality traits from the Big 5 Personality Model, which biases their generated responses.

To address challenges with ingesting free-form text input, which can prompt LLM responses beyond the scope of the game narrative, PANGeA includes a novel validation system. This system uses the LLM's intelligence to evaluate text input and align generated responses with the unfolding narrative. PANGeA's custom memory system stores game data, providing context to augment the generated responses and maintain narrative consistency.

PANGeA's server has a REST interface, enabling any game engine to directly integrate with it. The server also supports the use of local LLMs or private models like OpenAI's.

An empirical study and ablation test of two versions of a demo game, Dark Shadows, demonstrate PANGeA's ability to generate narrative-consistent content even when provided varied and unpredictable, free-form text input. Without the validation system, the LLM frequently generated out-of-scope responses.

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Principais Insights Extraídos De

PANGeA: Procedural Artificial Narrative using Generative AI for Turn-Based Video Games

by Steph Buongi... às arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19721.pdf

Perguntas Mais Profundas

How could PANGeA's approach be extended to generate narrative content for other game genres beyond turn-based RPGs?

PANGeA's approach to procedural narrative generation can be extended to generate narrative content for various game genres beyond turn-based RPGs by adapting its structured framework to suit the specific requirements of different genres. For example:

Action Games: PANGeA can be modified to generate dynamic narratives that align with fast-paced action sequences, incorporating player choices and environmental interactions that enhance the gameplay experience.

Adventure Games: For adventure games, PANGeA can focus on creating immersive storylines with branching narratives based on player decisions, leading to multiple outcomes and endings.

Simulation Games: In simulation games, PANGeA can generate narrative content that reflects the evolving scenarios and player-driven events, providing a more personalized and engaging experience.

Strategy Games: PANGeA can be tailored to generate narrative elements that align with strategic decision-making, offering players strategic challenges and consequences based on their choices.

Horror Games: For horror games, PANGeA can create suspenseful and chilling narratives, adapting the generated content to evoke fear and tension in players through atmospheric storytelling and character interactions.

By customizing PANGeA's prompting schema, validation system, and memory mechanisms to suit the specific storytelling requirements of different game genres, developers can leverage its capabilities to enhance narrative content generation across a wide range of gaming experiences.

What potential ethical considerations should be addressed when using large language models to generate game narratives, particularly around the representation of personality traits and character biases?

When using large language models (LLMs) like PANGeA to generate game narratives, several ethical considerations should be addressed to ensure responsible and inclusive storytelling:

Bias Mitigation: Developers must actively work to mitigate biases present in LLMs to prevent the perpetuation of stereotypes or discriminatory representations in game narratives. This includes regular audits of generated content and the implementation of bias detection algorithms.

Representation: Careful consideration should be given to how personality traits and character biases are portrayed in the game. Ensuring diverse and authentic representations of different identities can promote inclusivity and avoid reinforcing harmful stereotypes.

Informed Consent: Players should be informed about the use of AI-generated content in games and have the option to opt-out of certain narrative elements if they find them uncomfortable or inappropriate.

Transparency: Game developers should be transparent about the use of LLMs in generating narratives, providing clear information on how AI technologies influence storytelling and character interactions.

User Empowerment: Players should have agency in shaping the narrative direction and character development, allowing them to influence the story outcomes and challenge predefined biases in the game.

By proactively addressing these ethical considerations, developers can create more inclusive and socially responsible game narratives that respect diverse perspectives and promote positive player experiences.

How might PANGeA's techniques for maintaining narrative consistency be applied to other domains beyond video games, such as interactive fiction or conversational AI assistants?

PANGeA's techniques for maintaining narrative consistency can be applied to various domains beyond video games, such as interactive fiction or conversational AI assistants, to enhance storytelling and user engagement:

Interactive Fiction: In interactive fiction, PANGeA's validation system can ensure that user choices and inputs align with the narrative arc, leading to coherent and immersive storytelling experiences. By validating user-generated content against predefined narrative rules, interactive fiction can offer dynamic and engaging narratives that respond to player interactions.

Conversational AI Assistants: PANGeA's memory system can be leveraged in conversational AI assistants to provide contextually relevant responses based on previous interactions with users. By storing and retrieving information from past conversations, AI assistants can offer more personalized and coherent responses, enhancing the overall user experience.

Educational Applications: PANGeA's structured approach to narrative generation can be utilized in educational applications to create interactive learning experiences. By aligning generated content with educational objectives and user inputs, PANGeA can support personalized and engaging learning environments.

Virtual Storytelling Platforms: PANGeA's techniques can be integrated into virtual storytelling platforms to enable users to co-create narratives in real-time. By validating user contributions and maintaining narrative consistency, these platforms can facilitate collaborative storytelling experiences across diverse audiences.

By adapting PANGeA's methodologies to different domains, organizations can leverage its capabilities to create interactive and engaging experiences that prioritize narrative coherence, user agency, and personalized interactions.