Conference proceeding
Interactive Visualizer to Facilitate Game Designers in Understanding Machine Learning
CHI EA '19 EXTENDED ABSTRACTS: EXTENDED ABSTRACTS OF THE 2019 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, pp 1-6
01 Jan 2019
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.
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Details
- Title
- Interactive Visualizer to Facilitate Game Designers in Understanding Machine Learning
- Creators
- Jiachi Xie - Drexel UniversityJichen Zhu - Drexel UniversityChelsea M. Myers - Drexel UniversityAssoc Comp Machinery
- Publication Details
- CHI EA '19 EXTENDED ABSTRACTS: EXTENDED ABSTRACTS OF THE 2019 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, pp 1-6
- Conference
- CHI 2019: CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS
- Publisher
- Assoc Computing Machinery
- Number of pages
- 6
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Digital Media
- Web of Science ID
- WOS:000482042102027
- Scopus ID
- 2-s2.0-85067298704
- Other Identifier
- 991019169572804721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Cybernetics
- Computer Science, Theory & Methods