Name
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 ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TG.2018.2846639 | IEEE Transactions on Games |
Keywords | DocType | Volume |
Games,Machine learning,Training,Machine learning algorithms,Neural networks,Maintenance engineering,Media | Journal | 10 |
Issue | ISSN | Citations |
3 | 2475-1502 | 31 |
PageRank | References | Authors |
1.23 | 36 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adam J. Summerville | 1 | 65 | 10.68 |
| Sam Snodgrass | 2 | 78 | 12.79 |
| Matthew Guzdial | 3 | 70 | 14.75 |
| Christoffer Holmgård | 4 | 99 | 10.81 |
| Amy K. Hoover | 5 | 93 | 8.28 |
| Aaron Isaksen | 6 | 58 | 5.94 |
| andrew nealen | 7 | 1175 | 53.78 |
| Julian Togelius | 8 | 2765 | 219.94 |