论文标题
实体嵌入游戏表示
Entity Embedding as Game Representation
论文作者
论文摘要
通过机器学习（PCGML）生成程序内容已显示出成功用机器学习生成新的视频游戏内容的成功。但是，大多数工作都集中在静态游戏内容的生产上，包括游戏水平和视觉元素。动态游戏内容（例如游戏机制）的工作要少得多。原因之一是缺乏动态游戏内容的一致表示，这对于多种统计机器学习方法是关键。我们提出了一种自动编码器，用于推导我们所谓的“实体嵌入”，这是一种一致的方式，是在同一表示中表示多个游戏中不同动态实体的一致方法。在本文中，我们介绍了博学的代表，以及一些证据表明其质量和未来的效用。
Procedural content generation via machine learning (PCGML) has shown success at producing new video game content with machine learning. However, the majority of the work has focused on the production of static game content, including game levels and visual elements. There has been much less work on dynamic game content, such as game mechanics. One reason for this is the lack of a consistent representation for dynamic game content, which is key for a number of statistical machine learning approaches. We present an autoencoder for deriving what we call "entity embeddings", a consistent way to represent different dynamic entities across multiple games in the same representation. In this paper we introduce the learned representation, along with some evidence towards its quality and future utility.
