Automated Game Design Learning
July 11, 2017 Β· Declared Dead Β· π IEEE Conference on Computational Intelligence and Games
"No code URL or promise found in abstract"
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Authors
Joseph C Osborn, Adam Summerville, Michael Mateas
arXiv ID
1707.03333
Category
cs.AI: Artificial Intelligence
Citations
18
Venue
IEEE Conference on Computational Intelligence and Games
Last Checked
4 months ago
Abstract
While general game playing is an active field of research, the learning of game design has tended to be either a secondary goal of such research or it has been solely the domain of humans. We propose a field of research, Automated Game Design Learning (AGDL), with the direct purpose of learning game designs directly through interaction with games in the mode that most people experience games: via play. We detail existing work that touches the edges of this field, describe current successful projects in AGDL and the theoretical foundations that enable them, point to promising applications enabled by AGDL, and discuss next steps for this exciting area of study. The key moves of AGDL are to use game programs as the ultimate source of truth about their own design, and to make these design properties available to other systems and avenues of inquiry.
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