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

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 gam...

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Bibliographic Details
Main Authors: Rupp, Florian, Puddu, Alessandro, Becker-Asano, Christian, Eckert, Kai
Format: Conference Proceeding
Language:English
Subjects:
game balancing
Games
human evaluation
Manuals
Optimization
playtesting
Procedural generation
Reinforcement learning
Statistical analysis
survey
Surveys
Online Access:Request full text
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Summary: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.
ISSN:2325-4289
DOI:10.1109/CoG60054.2024.10645642