This paper focuses on procedurally generating rules and communicating them to
players to adjust the difficulty. This is part of a larger project to collect
and adapt games in educational games for young children using a digital puzzle
game designed for kindergarten. A genetic algorithm is used together with a
difficulty measure to find a target number of solution sets and a large
language model is used to communicate the rules in a narrative context. During
testing the approach was able to find rules that approximate any given target
difficulty within two dozen generations on average. The approach was combined
with a large language model to create a narrative puzzle game where players
have to host a dinner for animals that can't get along. Future experiments will
try to improve evaluation, specialize the language model on children's
literature, and collect multi-modal data from players to guide adaptation.

本文研究了利用基因算法和自然语言模型生成规则，将其应用于教育游戏的难度调整。通过应用该方法设计了一款趣味教育游戏，针对幼儿园儿童，且初步实验结果表明该方法能够在二十多代以内找到符合指定难度要求的规则。未来研究将关注数据收集与模型优化。