Abstract
Human playtesting is a useful step in the game development process, but involves high economic costs and is time-consuming. While playtesting through artificial intelligence is gaining attention, it is challenging to analyze the collected data. We address the challenge by proposing visualizations to derive insights about level design in 2D side-scrolling games. To focus on the navigation behavior in the level design, we study the trajectories of computer agents trained using artificial intelligence. We demonstrate the effectiveness of our approach by implementing a web-based prototype and presenting the insights gained from the visualizations for the game Sonic the Hedgehog 2. We highlight lessons learned and directions to customize the approach for other analysis goals of playtesting.
Original language | English |
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Title of host publication | IEEE Conference on Games, CoG 2020 |
Publisher | IEEE Computer Society |
Pages | 572-575 |
Number of pages | 4 |
ISBN (Electronic) | 9781728145334 |
DOIs | |
Publication status | Published - Aug 2020 |
Event | 2020 IEEE Conference on Games, CoG 2020 - Virtual, Osaka, Japan Duration: 24 Aug 2020 → 27 Aug 2020 |
Conference
Conference | 2020 IEEE Conference on Games, CoG 2020 |
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Country | Japan |
City | Virtual, Osaka |
Period | 24/08/20 → 27/08/20 |
Keywords
- artificial intelligence
- playtesting
- visualization