Grammar-based Game Description Generation using Large Language Models
This work addresses the problem of automated game description generation for researchers and developers in AI and game design, representing an incremental advancement over existing methods.
The paper tackles the challenge of generating Game Description Language (GDL) descriptions from natural language by introducing a framework that uses Large Language Models (LLMs) with a two-stage process involving grammar generation and iterative refinement. The results show that this iterative improvement approach significantly outperforms baseline methods that directly use LLM outputs.
Game Description Language (GDL) provides a standardized way to express diverse games in a machine-readable format, enabling automated game simulation, and evaluation. While previous research has explored game description generation using search-based methods, generating GDL descriptions from natural language remains a challenging task. This paper presents a novel framework that leverages Large Language Models (LLMs) to generate grammatically accurate game descriptions from natural language. Our approach consists of two stages: first, we gradually generate a minimal grammar based on GDL specifications; second, we iteratively improve the game description through grammar-guided generation. Our framework employs a specialized parser that identifies valid subsequences and candidate symbols from LLM responses, enabling gradual refinement of the output to ensure grammatical correctness. Experimental results demonstrate that our iterative improvement approach significantly outperforms baseline methods that directly use LLM outputs. Our code is available at https://github.com/tsunehiko/ggdg