Name
Title | ||
|---|---|---|
Interactive Visualizer to Facilitate Game Designers in Understanding Machine Learning. |
Abstract | ||
|---|---|---|
Machine Learning (ML) is a useful tool for modern game designers but often requires a technical background to understand. This gap of knowledge can intimidate less technical game designers from employing ML techniques to evaluate designs or incorporate ML into game mechanics. Our research aims to bridge this gap by exploring interactive visualizations as a way to introduce ML principles to game designers. We have developed QUBE, an interactive level designer that shifts ML education into the context of game design. We present QUBE's interactive visualization techniques and evaluation through two expert panels (n=4, n=6) with game design, ML, and user experience experts.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3290607.3312851 | CHI Extended Abstracts |
Keywords | Field | DocType |
game design, interactive visualizations, machine learning | User experience design,Game mechanics,Computer science,Game design,Human–computer interaction,Interactive visualization,Artificial intelligence,Multimedia,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-5971-9 | 1 | 0.40 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiachi Xie | 1 | 1 | 0.40 |
| Chelsea Myers | 2 | 11 | 2.66 |
| Jichen Zhu | 3 | 111 | 29.76 |