Tile Embedding: A General Representation for Procedural Level Generation via Machine Learning

• URL: http://arxiv.org/abs/2110.03181v1
• Date: Thu, 7 Oct 2021 04:48:48 GMT
• Title: Tile Embedding: A General Representation for Procedural Level Generation via Machine Learning
• Authors: Mrunal Jadhav and Matthew Guzdial
• Abstract summary: We present tile embeddings, a unified, affordance-rich representation for tile-based 2D games. We employ autoencoders trained on the visual and semantic information of tiles from a set of existing, human-annotated games. We evaluate this representation on its ability to predict affordances for unseen tiles, and to serve as a PLGML representation for annotated and unannotated games.
• Score: 1.590611306750623
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, Procedural Level Generation via Machine Learning (PLGML) techniques have been applied to generate game levels with machine learning. These approaches rely on human-annotated representations of game levels. Creating annotated datasets for games requires domain knowledge and is time-consuming. Hence, though a large number of video games exist, annotated datasets are curated only for a small handful. Thus current PLGML techniques have been explored in limited domains, with Super Mario Bros. as the most common example. To address this problem, we present tile embeddings, a unified, affordance-rich representation for tile-based 2D games. To learn this embedding, we employ autoencoders trained on the visual and semantic information of tiles from a set of existing, human-annotated games. We evaluate this representation on its ability to predict affordances for unseen tiles, and to serve as a PLGML representation for annotated and unannotated games.

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