论文标题
使用神经进化和新颖的视频游戏水平来生成程序内容
Procedural Content Generation using Neuroevolution and Novelty Search for Diverse Video Game Levels
论文作者
论文摘要
程序生成的视频游戏内容有可能大大减少游戏开发人员和大型工作室的内容创建预算。但是，采用受到诸如缓慢生成以及低质量和内容多样性之类的限制的阻碍。我们介绍了一种基于进化搜索的方法，用于使用新颖性搜索来进化水平发电机，以实时生成各种水平，而无需培训数据或详细的领域特定知识。我们在两个域上测试了我们的方法，我们的结果表明，与现有方法相比，在获得可比的度量分数的同时，生成时间的速度有一个数量级加速。我们进一步证明了在不进行重新培训的情况下将其推广到任意大小的水平的能力。
Procedurally generated video game content has the potential to drastically reduce the content creation budget of game developers and large studios. However, adoption is hindered by limitations such as slow generation, as well as low quality and diversity of content. We introduce an evolutionary search-based approach for evolving level generators using novelty search to procedurally generate diverse levels in real time, without requiring training data or detailed domain-specific knowledge. We test our method on two domains, and our results show an order of magnitude speedup in generation time compared to existing methods while obtaining comparable metric scores. We further demonstrate the ability to generalise to arbitrary-sized levels without retraining.
