Rupp, FlorianStolzenburg, Frieder2023-09-202023-09-202023https://dl.gi.de/handle/20.500.12116/42405Games, including board games and video games, are widely popular and serve as a platform for entertainment while also challenging various cognitive abilities. A game, especially if it’s a competitive multiplayer setting, needs to be balanced in order to provide a joyful experience for all players. The balancing process of such game levels, however, requires a lot of work and manual testing. To address this shortcoming, this thesis aims to implement methods to automatically generate balanced game levels. Therefore, four research questions with ideas for problem-solving approaches are presented: (1) the development and evaluation of metrics to measure the balancing state of a game, (2) research on methods for learning the procedural generation of balanced levels, (3) balance levels for players with different strategies and, (4) examine how findings can be applied to other research areas. Methods from the field of procedural content generation, especially in combination with machine learning methods, are promising to answer these questions. In a first paper, I already introduced a reinforcement learning based approach to create balanced levels for two players.enGames; Balancing; Procedural Content Generation; Reinforcement LearningText10.18420/ki2023-dc-05