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- W4384261712 abstract "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." @default.
- W4384261712 created "2023-07-14" @default.
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- W4384261712 date "2023-07-07" @default.
- W4384261712 modified "2023-09-25" @default.
- W4384261712 title "Procedurally generating rules to adapt difficulty for narrative puzzle games" @default.
- W4384261712 doi "https://doi.org/10.48550/arxiv.2307.05518" @default.
- W4384261712 hasPublicationYear "2023" @default.
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