Högskolan i Skövde

Game balancing is an essential part of game design, and it plays a vital role as the balancing results directly affect the players' experiences. At present, there is no well-admitted definition of game balance. Generally, the game designers hold their own envisions of “balancing” in specific contextualized environments. Accordingly, it is a great challenge for game designers/developers to understand and establish. Since game balance is a multifaceted concept, there is no prescriptive guideline either. In this work, numerical balancing is selected to investigate the possibility of applying engineering optimization techniques to the game unit balancing in a quantitative manner. Currently, although the definite rules of game mechanics could be expressed mathematically, the game developers tune different attributes manually to evaluate the changes in an iterative way, which is tedious and time-consuming. Moreover, the balancing quality and efficiency strongly rely on the game developers' personal skills. To address this issue, we modeled the balancing process as a combinatorial optimization problem, in which the assessment metrics of game units are optimization objectives, and the attributes of game units are variables. The “balancing” of game units is interpreted as minimizing the standard deviations of the assessment metrics, and the evolutionary algorithm such as NSGA-II is used to find the optimization solutions. A typical case study is employed to demonstrate the proposed idea, and the optimization results show that the proposed method can simultaneously improve the numerical balancing in terms of quality and efficiency. At last, we summarize the contributions of the work and discuss how the proposed method can be further improved for game balancing in future work.