Intentional computational level design
Intentional computational level design
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DOI：
10.1145/3321707.3321849
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发表时间：
2019-04
期刊：
影响因子：
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通讯作者：
A. Khalifa;M. Green;Gabriella A. B. Barros;J. Togelius
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文献类型：
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作者：
A. Khalifa;M. Green;Gabriella A. B. Barros;J. Togelius
视频游戏中的级别和内容的程序生成是一个具有挑战性的AI问题。这种一代通常依赖于评估所生成的内容的智能方式，以使约束得到满足和/或目标最大化。在这项工作中，我们解决了创建不仅可以播放而且围绕游戏中特定机制的级别的问题。我们使用约束的进化算法和质量多样性算法来生成称为场景的超级马里奥兄弟级别的小部分，使用三种不同的模拟方法：有限的试剂，惩罚模型和力学维度。这三种方法都能够创建场景，从而为玩家提供机会遇到或使用具有不同属性的目标力学。我们通过讨论每种方法的优点和缺点，并将其相互比较来结束。
The procedural generation of levels and content in video games is a challenging AI problem. Often such generation relies on an intelligent way of evaluating the content being generated so that constraints are satisfied and/or objectives maximized. In this work, we address the problem of creating levels that are not only playable but also revolve around specific mechanics in the game. We use constrained evolutionary algorithms and quality-diversity algorithms to generate small sections of Super Mario Bros levels called scenes, using three different simulation approaches: Limited Agents, Punishing Model, and Mechanics Dimensions. All three approaches are able to create scenes that give opportunity for a player to encounter or use targeted mechanics with different properties. We conclude by discussing the advantages and disadvantages of each approach and compare them to each other.