It might be balanced, but is it actually good? An Empirical Evaluation of Game Level Balancing

TL;DR

Findings indicate that the PCGRL-based balancing positively influences players’ perceived balance for most scenarios, albeit with differences in aspects of the balancing between scenarios.

Abstract

Achieving optimal balance in games is essential to their success, yet reliant on extensive manual work and playtesting. To facilitate this process, the Procedural Content Generation via Reinforcement Learning (PCGRL) framework has recently been effectively used to improve the balance of existing game levels. This approach, however, only assesses balance heuristically, neglecting actual human perception. For this reason, this work presents a survey to empirically evaluate the created content paired with human playtesting. Participants in four different scenarios are asked about their perception of changes made to the level both before and after balancing, and vice versa. Based on descriptive and statistical analysis, our findings indicate that the PCGRL-based balancing positively influences players' perceived balance for most scenarios, albeit with differences in aspects of the balancing between scenarios.

Figures (2)

• Figure 1: The four scenarios included for playtesting. Levels in scenarios 1-3 were balanced ($=0.5$) from a previously unbalanced version ($\neq 0.5$) by swapping tiles using the method from Rupp et al. rupp_balancing_2023 (cf. Section \ref{['sec:level-balancing']}). Scenario 4 presents the balanced version of scenario 2, which was subsequently unbalanced again using the PCGRL model.
• Figure 2: The design of the survey. Each participant is randomly assigned a scenario and which version of the level to play first.