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Journal article
Procedural Content Generation via Machine Learning (PCGML)
IEEE transactions on games, v 10(3), pp 257-270
01 Sep 2018
Abstract
This survey explores procedural content generation via machine learning (PCGML), defined as the generation of game content using machine learning models trained on existing content. As the importance of PCG for game development increases, researchers explore new avenues for generating high-quality content with or without human involvement; this paper addresses the relatively new paradigm of using machine learning (in contrast with search-based, solver-based, and constructive methods). We focus on what is most often considered functional game content, such as platformer levels, game maps, interactive fiction stories, and cards in collectible card games, as opposed to cosmetic content, such as sprites and sound effects. In addition to using PCG for autonomous generation, cocreativity, mixed-initiative design, and compression, PCGML is suited for repair, critique, and content analysis because of its focus on modeling existing content. We discuss various data sources and representations that affect the generated content. Multiple PCGML methods are covered, including neural networks: long short-term memory networks, autoencoders, and deep convolutional networks; Markov models: n-grams and multidimensional Markov chains; clustering; and matrix factorization. Finally, we discuss open problems in PCGML, including learning from small data sets, lack of training data, multilayered learning, style-transfer, parameter tuning, and PCG as a game mechanic.
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Details
- Title
- Procedural Content Generation via Machine Learning (PCGML)
- Creators
- Adam Summerville - Univ Calif Santa Cruz, Dept Computat Media, Santa Cruz, CA 95064 USAMatthew Guzdial - Georgia Institute of TechnologyChristoffer HolmgardAmy K. Hoover - Northeastern UniversityAaron Isaksen - New York UniversityAndy Nealen - New York UniversityJulian Togelius - New York University
- Publication Details
- IEEE transactions on games, v 10(3), pp 257-270
- Publisher
- IEEE
- Number of pages
- 14
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- College of Computing and Informatics
- Web of Science ID
- WOS:000447381400002
- Scopus ID
- 2-s2.0-85074941654
- Other Identifier
- 991021880112704721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Software Engineering