Matthew Guzdial

TL;DR: In this paper, a survey explores procedural content generation via machine learning (PCGML), defined as the generation of game content using machine learning models trained on existing content, focusing on what is most often considered functional game content, such as platformer levels, game maps, interactive fiction stories and cards in collectible card games.

TL;DR: This paper addresses the relatively new paradigm of using machine learning (in contrast with search-based, solver- based, and constructive methods), and focuses on what is most often considered functional game content, such as platformer levels, game maps, interactive fiction stories, and cards in collectible card games.

TL;DR: An unsupervised process to generate full video game levels from a model trained on gameplay video that represents probabilistic relationships between shapes properties, and relates the relationships to stylistic variance within a domain is presented.

TL;DR: The design of the Morai Maker intelligent tool is discussed, which developed a game level design tool for Super Mario Bros.-style games with a built-in AI level designer and found that level designers vary in their desired interactions with, and role of, the AI.

TL;DR: This work presents a novel approach to learn a forward simulation model via simple search over pixel input and demonstrates the significant improvement in predicting future states compared with a baseline CNN and applies the learned model to train a game playing agent.