Exploring Level Blending across Platformers via Paths and Affordances

Techniques for procedural content generation via machine learning (PCGML) have been shown to be useful for generating novel game content. While used primarily for producing new content in the style of the game domain used for training, recent works have increasingly started to explore methods for discovering and generating content in novel domains via techniques such as level blending and domain transfer. In this paper, we build on these works and introduce a new PCGML approach for producing novel game content spanning multiple domains. We use a new affordance and path vocabulary to encode data from six different platformer games and train variational autoencoders on this data, enabling us to capture the latent level space spanning all the domains and generate new content with varying proportions of the different domains.Comment: 6 pages, 5 figures, 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2020

Comparing PCG metrics with Human Evaluation in Minecraft Settlement Generation

© 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1145/3472538.3472590There are a range of metrics that can be applied to the artifacts produced by procedural content generation, and several of themcome with qualitative claims. In this paper, we adapt a range of existing PCG metrics to generated Minecraft settlements, developa few new metrics inspired by PCG literature, and compare the resulting measurements to existing human evaluations. The aim isto analyze how those metrics capture human evaluation scores in different categories, how the metrics generalize to another gamedomain, and how metrics deal with more complex artifacts. We provide an exploratory look at a variety of metrics and providean information gain and several correlation analyses. We found some relationships between human scores and metrics countingspecific elements, measuring the diversity of blocks and measuring the presence of crafting materials for the present complex blocks

Comparing PCG metrics with Human Evaluation in Minecraft Settlement Generation

There are a range of metrics that can be applied to the artifacts produced by procedural content generation, and several of them come with qualitative claims. In this paper, we adapt a range of existing PCG metrics to generated Minecraft settlements, develop a few new metrics inspired by PCG literature, and compare the resulting measurements to existing human evaluations. The aim is to analyze how those metrics capture human evaluation scores in different categories, how the metrics generalize to another game domain, and how metrics deal with more complex artifacts. We provide an exploratory look at a variety of metrics and provide an information gain and several correlation analyses. We found some relationships between human scores and metrics counting specific elements, measuring the diversity of blocks and measuring the presence of crafting materials for the present complex blocks.Comment: Accepted to the FDG'21 workshop on PC