Skip to main content
eScholarship
Open Access Publications from the University of California

UC Santa Cruz

UC Santa Cruz Electronic Theses and Dissertations bannerUC Santa Cruz

Learning from Games for Generative Purposes

Creative Commons 'BY-NC-SA' version 4.0 license
Abstract

Procedural Content Generation has been a part of videogames for most of their existence. Early use allowed games to present more content than would have been possible to store, but modern use has blossomed in to a field of research with a wide variety of goals. One such goal has been the generation of content matching a particular design style, and most of the research has been focused on classic AI methods -- such as search and constraint satisfaction -- to produce content optimizing for some set of constraints and metrics that a researcher sets forth. Recently, there has been a focus on using machine learning to learn the design latent within the content itself, so that a researcher does not need to encode that design knowledge them self; however, this work still includes a large amount of human annotation and guidance.

This dissertation focuses on two systems, \textit{Mappy} and \textit{Kamek}, which can work together to learn from observation of a game, so as to be able to generate content that has similar properties as that of the original content. \textit{Mappy} is an AI system that uses a wide range of techniques to emulate the process that a human undertakes when they play, observe, and learn from a game. \textit{Kamek} is a machine learning system that learns from game levels and generates new levels of high quality. This dissertation also covers a suite of methodologies for the analysis and presentation of procedurally generated content -- with a specific focus on machine learned generators.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View