Automatic Playtesting for Game Parameter Tuning via Active Learning
  Game designers use human playtesting to gather feedback about game design
elements when iteratively improving a game. Playtesting, however, is expensive:
human testers must be recruited, playtest results must be aggregated and
interpreted, and changes to game designs must be extrapolated from these
results. Can automated methods reduce this expense? We show how active learning
techniques can formalize and automate a subset of playtesting goals.
Specifically, we focus on the low-level parameter tuning required to balance a
game once the mechanics have been chosen. Through a case study on a
shoot-`em-up game we demonstrate the efficacy of active learning to reduce the
amount of playtesting needed to choose the optimal set of game parameters for
two classes of (formal) design objectives. This work opens the potential for
additional methods to reduce the human burden of performing playtesting for a
variety of relevant design concerns.
