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Player-Driven Emergence in LLM-Driven Game Narrative (2404.17027v3)

Published 25 Apr 2024 in cs.CL and cs.AI

Abstract: We explore how interaction with LLMs can give rise to emergent behaviors, empowering players to participate in the evolution of game narratives. Our testbed is a text-adventure game in which players attempt to solve a mystery under a fixed narrative premise, but can freely interact with non-player characters generated by GPT-4, a LLM. We recruit 28 gamers to play the game and use GPT-4 to automatically convert the game logs into a node-graph representing the narrative in the player's gameplay. We find that through their interactions with the non-deterministic behavior of the LLM, players are able to discover interesting new emergent nodes that were not a part of the original narrative but have potential for being fun and engaging. Players that created the most emergent nodes tended to be those that often enjoy games that facilitate discovery, exploration and experimentation.

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Authors (13)
  1. Xiangyu Peng (33 papers)
  2. Jessica Quaye (6 papers)
  3. Weijia Xu (23 papers)
  4. Chris Brockett (37 papers)
  5. Bill Dolan (45 papers)
  6. Nebojsa Jojic (43 papers)
  7. Gabriel DesGarennes (2 papers)
  8. Ken Lobb (2 papers)
  9. Michael Xu (18 papers)
  10. Jorge Leandro (3 papers)
  11. Claire Jin (4 papers)
  12. Sudha Rao (23 papers)
  13. Portia Botchway (3 papers)
Citations (7)
View on Semantic Scholar

Summary

Player-Driven Emergence in LLM-Driven Game Narrative

The paper, "Player-Driven Emergence in LLM-Driven Game Narrative," explores the interplay between LLMs and player agency in the context of text-based games, focusing on how LLMs can promote emergent gameplay and narratives. This work specifically investigates the implementation of GPT-4 within a text-adventure game called "Dejaboom!" to assess the model's role in facilitating novel narrative pathways through player interactions.

Core Concepts and Methodology

The research revolves around integrating LLMs to dynamically generate non-player character (NPC) dialogue in real-time, providing players with an unprecedented level of interaction flexibility. This dynamic interaction aims to transcend traditional predefined game dialogues, thus offering a richer set of narrative possibilities. Through engagement with GPT-4, players can diverge from the fixed narrative tree, thereby introducing emergent elements that enhance gameplay experience.

A user paper was conducted with 28 participants, highlighting how players' interactions with the model led to creative deviations from the original narrative. By converting gameplay logs into a narrative graph, the paper identifies "emergent nodes"—unexpected narrative elements introduced by players as they explore new strategies within the game environment.

Findings

The analysis revealed various categories of emergent nodes introduced, such as:

  • Extracting Information from NPCs: Players employed novel strategies to garner information from NPCs, such as deceit or manipulation, which were not originally anticipated by the game designers.
  • Suggestions for Additional Assets: Players sought enhancements in the game world, implicitly suggesting the inclusion of new objects or NPCs.
  • Creative Problem Solving: Players devised inventive ways to uncover hidden information or achieve game objectives, reflecting their problem-solving approaches.

These observations underline the potential for player-driven emergence, facilitated by the LLM's non-deterministic nature, to enrich player experience and offer valuable insights to game designers for narrative expansion.

Implications and Future Directions

The implications of this research are multi-faceted. Practically, it provides a blueprint for integrating LLMs in game narrative design, empowering designers to adopt a more iterative, player-centered approach. Theoretically, it suggests a framework for understanding how AI can augment creative processes in gaming, expanding the sphere of narrative possibilities.

From a development perspective, the paper indicates that incorporating LLM-driven dialogue can foster a more engaging and exploratory gaming environment. The categorization of emergent nodes offers designers insights into player behavior, which could influence future game mechanics and narrative design. Additionally, this player-driven feedback can be integral in refining the interactive elements of games, leading to more sophisticated AI-driven experiences.

Moreover, by correlating player motivation with the tendency to create emergent nodes, the paper hints at a personalized gaming experience where AI tailors narrative complexity to individual player profiles. Future explorations could investigate this personalization further, optimizing AI responses based on player interaction patterns.

Conclusion

The paper successfully illustrates the potential of LLMs for facilitating emergent gameplay through player-NPC interactions. By analyzing the emergent pathways introduced by players, designers can gain insights into crafting more dynamic and inclusive game worlds. This work highlights the evolving role of AI in interactive entertainment, paving the way for future innovations in game design that leverage the synergy between human creativity and AI capabilities.

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