  To lower the barriers to game design development, automated game design,
which generates game designs through computational processes, has been
explored. In automated game design, machine learning-based techniques such as
evolutionary algorithms have achieved success. Benefiting from the remarkable
advancements in deep learning, applications in computer vision and natural
language processing have progressed in level generation. However, due to the
limited amount of data in game design, the application of deep learning has
been insufficient for tasks such as game description generation. To pioneer a
new approach for handling limited data in automated game design, we focus on
the in-context learning of large language models (LLMs). LLMs can capture the
features of a task from a few demonstration examples and apply the capabilities
acquired during pre-training. We introduce the grammar of game descriptions,
which effectively structures the game design space, into the LLMs' reasoning
process. Grammar helps LLMs capture the characteristics of the complex task of
game description generation. Furthermore, we propose a decoding method that
iteratively improves the generated output by leveraging the grammar. Our
experiments demonstrate that this approach performs well in generating game
descriptions.
