Automatic Playtesting for Game Parameter Tuning via Active Learning

TL;DR

This paper explores how active learning can automate and reduce the cost of game parameter tuning during playtesting, demonstrating effectiveness in a case study on a shoot-`em-up game.

Contribution

It introduces the application of active learning techniques to automate low-level game parameter tuning, reducing the need for extensive human playtesting.

Findings

Active learning reduces playtesting effort for parameter tuning.

Effective in balancing game mechanics with fewer human tests.

Potential to automate various game design evaluation tasks.

Abstract

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.