Name
Abstract | ||
|---|---|---|
Machine learning has been a popular tool in many different fields, including procedural content generation. However, procedural content generation via machine learning (PCGML) approaches can struggle with controllability and coherence. In this paper, we attempt to address these problems by learning to generate human-like paths, and then generating levels based on these paths. We extract player path data from gameplay video, train an LSTM to generate new paths based on this data, and then generate game levels based on this path data. We demonstrate that our approach leads to more coherent levels for the game Lode Runner in comparison to an existing PCGML approach. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3472538.3472602 | FDG |
DocType | ISSN | Citations |
Conference | Proceedings of the Twelfth Workshop on Procedural Content
Generation 2021 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kynan Sorochan | 1 | 0 | 0.34 |
| Jerry Chen | 2 | 0 | 1.35 |
| Yakun Yu | 3 | 1 | 0.69 |
| Matthew Guzdial | 4 | 70 | 14.75 |