mlsanityData to 14 Oct 2026

You Have Thirteen Hours in Which to Solve the Labyrinth: Enhancing AI Game Masters with Function Calling

arXiv 2409.06949 ↗First posted 11 Sep 2024cs.CL cs.AIcited by 4

On the map: LLM agents and tool use → Reinforcement learning for LLM agents

Abstract (arXiv)

Developing a consistent and reliable AI game master for text-based games is a challenging task due to the limitations of large language models (LLMs) and the complexity of the game master's role. This paper presents a novel approach to enhance AI game masters by leveraging function calling in the context of the table-top role-playing game "Jim Henson's Labyrinth: The Adventure Game." Our methodology involves integrating game-specific controls through functions, which we show improves the narrative quality and state update consistency of the AI game master. The experimental results, based on human evaluations and unit tests, demonstrate the effectiveness of our approach in enhancing gameplay experience and maintaining coherence with the game state. This work contributes to the advancement of game AI and interactive storytelling, offering insights into the design of more engaging and consistent AI-driven game masters.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use. Builds on 10 of its 19 references that are in the map. First posted on arXiv 11 Sep 2024.

Builds on

10 of 19 references.

References · 10

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  4. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  5. 2022Dungeons and Dragons as a Dialog Challenge for Artificial Intelligencecited by 35
  6. 2024Large Language Models as Zero-shot Dialogue State Tracker through Function Callingcited by 34
  7. 2022I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragonscited by 22
  8. 2023FIREBALL: A Dataset of Dungeons and Dragons Actual-Play with Structured Game State Informationcited by 18
  9. 2023Kani: A Lightweight and Highly Hackable Framework for Building Language Model Applicationscited by 11
  10. 2023Rolling the Dice: Imagining Generative AI as a Dungeons & Dragons Storytelling Companioncited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2026World-State Transformations for Neuro-symbolic Interactive Storytellingcited by 2
  2. 2025How LLMs are Shaping the Future of Virtual Realitycited by 1
  3. 2025Deflanderization for Game Dialogue: Balancing Character Authenticity with Task Execution in LLM-based NPCscited by 0
  4. 2026Assessing Rule Adherence of LLM Adjudicators in Call of Cthulhu TRPGcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Static Vs. Agentic Game Master AI for Facilitating Solo Role-Playing Experiences
  2. 2026Playing With AI: How Do State-Of-The-Art Large Language Models Perform in the 1977 Text-Based Adventure Game Zork?
  3. 2025Baba is LLM: Reasoning in a Game with Dynamic Rules
  4. 2024LLMs May Not Be Human-Level Players, But They Can Be Testers: Measuring Game Difficulty with LLM Agents
  5. 2023Language as Reality: A Co-Creative Storytelling Game Experience in 1001 Nights using Generative AI
  6. 2026The World Won't Wait: Evaluating and Teaching Agents Under a Live Clock
  7. 2026BotzoneBench: Scalable LLM Evaluation via Graded AI Anchors
  8. 2023Tachikuma: Understading Complex Interactions with Multi-Character and Novel Objects by Large Language Models