Procedurally generating rules to adapt difficulty for narrative puzzle games

• URL: http://arxiv.org/abs/2307.05518v1
• Date: Fri, 7 Jul 2023 11:14:53 GMT
• Title: Procedurally generating rules to adapt difficulty for narrative puzzle games
• Authors: Thomas Volden, Djordje Grbic, Paolo Burelli
• Abstract summary: This paper focuses on procedurally generating rules and communicating them to players to adjust the difficulty. A genetic algorithm is used together with a difficulty measure to find a target number of solution sets. A large language model is used to communicate the rules in a narrative context.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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.

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