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

arXiv: https://arxiv.org/abs/2409.06949 · HTML: https://mlsanity.com/paper/2409.06949/

## 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.

## Placed in

- LLM agents and tool use / [Reinforcement learning for LLM agents](https://mlsanity.com/agents/reinforcement-learning-for-llm-agents/)

## Builds on (most cited first, 10 of 10)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Dungeons and Dragons as a Dialog Challenge for Artificial Intelligence](https://mlsanity.com/paper/2210.07109/) (2022)
- [Large Language Models as Zero-shot Dialogue State Tracker through Function Calling](https://mlsanity.com/paper/2402.10466/) (2024)
- [I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons](https://mlsanity.com/paper/2212.10060/) (2022)
- [FIREBALL: A Dataset of Dungeons and Dragons Actual-Play with Structured Game State Information](https://mlsanity.com/paper/2305.01528/) (2023)
- [Kani: A Lightweight and Highly Hackable Framework for Building Language Model Applications](https://mlsanity.com/paper/2309.05542/) (2023)
- [Rolling the Dice: Imagining Generative AI as a Dungeons & Dragons Storytelling Companion](https://mlsanity.com/paper/2304.01860/) (2023)

## Built on by (most cited first, 4 of 4)

- [World-State Transformations for Neuro-symbolic Interactive Storytelling](https://mlsanity.com/paper/2605.24719/) (2026)
- [How LLMs are Shaping the Future of Virtual Reality](https://mlsanity.com/paper/2508.00737/) (2025)
- [Deflanderization for Game Dialogue: Balancing Character Authenticity with Task Execution in LLM-based NPCs](https://mlsanity.com/paper/2510.13586/) (2025)
- [Assessing Rule Adherence of LLM Adjudicators in Call of Cthulhu TRPG](https://mlsanity.com/paper/2607.02802/) (2026)

## 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.