Game Mechanic Alignment Theory and Discovery
This work addresses the problem of automated tutorial generation in games for designers and players, though it appears incremental as it builds on existing frameworks like GVGAI.
The paper introduces Game Mechanic Alignment theory to organize game mechanics based on systemic rewards and agential motivations, aiming to improve automated tutorial generation by tailoring tutorials to specific playstyles or players. It applies this theory to well-known games and the GVGAI framework, demonstrating its utility for game designers.
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.