You Have Thirteen Hours in Which to Solve the Labyrinth: Enhancing AI Game Masters with Function Calling
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
SituatedThinker: Grounding LLM Reasoning with Real-World through Situated Thinking
TextArena
Persona Dynamics: Unveiling the Impact of Personality Traits on Agents in Text-Based Games
TextGames: Learning to Self-Play Text-Based Puzzle Games via Language Model Reasoning
STARLING: Self-supervised Training of Text-based Reinforcement Learning Agent with Large Language Models
EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learning
토론
이 논문과 연결된 포럼 글이 아직 없습니다.