Art der Publikation: Beitrag in Sammelwerk

Visualizing AI Playtesting Data of 2D Side-scrolling Games

Autor(en):

Agarwal, Shivam; Herrmann, Christian; Wallner, Günter; Beck, Fabian

Titel des Sammelbands:

Proceedings of IEEE Conference on Games

Seiten:

572-575

Veröffentlichung:

2020

Schlagworte:

artificial intelligence, playtesting, visualization

Digital Object Identifier (DOI):

doi:10.1109/CoG47356.2020.9231915

Volltext:

Visualizing AI Playtesting Data of 2D Side-scrolling Games (854 KB)

Vortrag zu dieser Publikation:

IEEE Conference on Games 2020

Aufzeichnung:

https://youtu.be/H6q7kLKfqRw

Zitation:

Download BibTeX

Kurzfassung

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.