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Title: Game mechanic alignment theory and discovery
Authors: Cerny Green, Michael
Khalifa, Ahmed
Bontrager, Philip
Canaan, Rodrigo
Togelius, Julian
Keywords: Computer games -- Design
Artificial intelligence
Nonparametric statistics
Intelligent tutoring systems
Issue Date: 2021
Publisher: Association for Computing Machinery
Citation: Cerny Green, M., Khalifa, A., Bontrager, P., Canaan, R., & Togelius, J. (2021). Game mechanic alignment theory and discovery. FDG '21: International Conference on the Foundations of Digital Games, Montreal.
Abstract: We present a new concept called Game Mechanic Alignment theory as a way to organize game mechanics through the lens of systemic rewards and agential motivations. By disentangling player and systemic influences, mechanics may be better identified for use in an automated tutorial generation system, which could tailor tutorials for a particular playstyle or player. Within, we apply this theory to several well-known games to demonstrate how designers can benefit from it, we describe a methodology for how to estimate "mechanic alignment", and we apply this methodology on multiple games in the GVGAI framework. We discuss how effectively this estimation captures agential motivations and systemic rewards and how our theory could be used as an alternative way to find mechanics for tutorial generation.
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