Procedurally generating rules to adapt difficulty for narrative puzzle games
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866916656214704128 |
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| author | Volden, Thomas Grbic, Djordje Burelli, Paolo |
| author_facet | Volden, Thomas Grbic, Djordje Burelli, Paolo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_05518 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Procedurally generating rules to adapt difficulty for narrative puzzle games Volden, Thomas Grbic, Djordje Burelli, Paolo Human-Computer Interaction Artificial Intelligence 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. |
| title | Procedurally generating rules to adapt difficulty for narrative puzzle games |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2307.05518 |