Static vs agentic AI in solo RPG experiences

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This paper offers a comprehensive exploration of how structured agentic AI, specifically through a multi-agent ReAct architecture, can deeply enhance player experiences. The approach detailed here is valuable for future AI-driven RPG developments, research in interactive fiction, and intelligent agent design.

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Title:

Static Vs. Agentic Game Master AI for Facilitating Solo Role-Playing Experiences

Authors:

  • Nicolai H. Jørgensen, Sarmilan Tharmabalan, Ilhan Aslan, Nicolai Brodersen Hansen, Timothy Merritt
    (Department of Computer Science, Aalborg University, Denmark)

Overview of the Paper:

The paper examines two types of Game Master (GM) AIs designed for facilitating single-player, text-based role-playing games (RPGs) inspired by games like Dungeons & Dragons (D&D). The goal was to enhance solo RPG experiences using Large Language Models (LLMs).

Core Contributions:

1. Two Distinct AI GM Versions:

  • Version 1 (Static): Uses simplified prompt engineering without active reasoning.
  • Version 2 (Agentic): Employs a sophisticated multi-agent architecture leveraging the ReAct framework, enabling advanced reasoning, action, and memory management.

2. Comparative Evaluation (User Study):

  • Involved 12 participants playing both AI GM versions.
  • Metrics used:
    • Player Experience Inventory (PXI): Assessed ease of control, immersion, curiosity, mastery, autonomy, etc.
    • Custom questions: Focused on narrative coherence, NPC interaction quality, story adaptation, and replayability.

Key Findings from Evaluation:

  • Version 2 (Agentic) significantly outperformed Version 1 on several key player experience measures:
    • Enhanced sense of immersion, curiosity, competence, and autonomy.
    • Improved narrative coherence, NPC engagement, and flexibility in story adaptation.
    • Players showed a clear preference for the Agentic system, especially valuing its ability to dynamically reason and respond.
  • Qualitative feedback highlighted Version 2’s ability to:
    • Provide more realistic and satisfying combat interactions.
    • Maintain a more coherent and immersive narrative.
    • Allow players to feel more autonomy and control.

Technical Insights:

  • Version 1 (Static Approach):
    • Relied entirely on extensive prompts containing all game-state information, resulting in:
      • Limited scalability due to context window constraints.
      • Narrative coherence issues as the game progressed.
  • Version 2 (Agentic Approach):
    • Used two specialized LLM agents:
      • Narrator Agent: Managed storytelling and direct interactions.
      • Archivist Agent: Handled game state management, tracking environments and NPCs for narrative continuity.
    • Leveraged structured JSON tool-calls within a ReAct framework, improving reasoning and decision-making.
    • Provided significant improvement in scalability and player experience.

Reflections and Future Work:

Reflections on ReAct and Multi-Agent Systems:

  • The ReAct framework’s structured reasoning process provided greater transparency and debugging capability.
  • Splitting responsibilities into separate agents (Narrator and Archivist) improved performance and response times.

Limitations Identified:

  • LLM API content filtering sometimes limited narrative flexibility.
  • Small sample size (N=12) limited statistical generalizability.
  • Allowing ties in preferences introduced ambiguity.

Suggested Future Directions:

  • Implementing narrative adherence tools to ensure plot consistency.
  • Enhancing accessibility (speech-to-text/text-to-speech).
  • Integrating affective computing to adapt narratives to emotional responses.
  • Expanding multimedia capabilities (audio, visuals) to complement text-based storytelling.

Conclusion:

The study demonstrates that a multi-agent AI GM (Agentic v2) substantially enriches the solo RPG experience, achieving closer alignment with human GM behaviors and player expectations. This approach marks significant progress toward fully immersive, intelligent, AI-driven narrative experiences in interactive fiction and role-playing gaming.

Practical Resource Provided:

Personal Observation:

This paper offers a comprehensive exploration of how structured agentic AI, specifically through a multi-agent ReAct architecture, can deeply enhance player experiences. The approach detailed here is valuable for future AI-driven RPG developments, research in interactive fiction, and intelligent agent design.

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