Entity Embedding as Game Representation

TL;DR

This paper introduces 'entity embeddings', a learned representation for dynamic game entities across multiple games, aiming to facilitate machine learning approaches in generating and understanding game mechanics.

Contribution

The paper presents a novel autoencoder-based method for creating consistent entity embeddings applicable across various games, addressing a key gap in dynamic game content representation.

Findings

Entity embeddings provide a unified representation for game entities.

Preliminary evidence suggests the embeddings capture meaningful features.

Potential for improved machine learning in game content generation.

Abstract

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.