Expanding Horizons of Level Diversity via Multi-objective Evolutionary Learning
This work addresses the need for more comprehensive level diversity in game generation, which is incremental by applying multi-objective evolutionary learning to an existing domain.
The paper tackled the problem of generating diverse game levels by formulating model training as a multi-objective learning problem, optimizing multiple diversity metrics simultaneously, and demonstrated on Super Mario Bros. that the framework enhances multi-dimensional diversity and identifies a Pareto front of generative models with tradeoffs among playability and diversity metrics.
In recent years, the generation of diverse game levels has gained increasing interest, contributing to a richer and more engaging gaming experience. A number of level diversity metrics have been proposed in literature, which are naturally multi-dimensional, leading to conflicted, complementary, or both relationships among these dimensions. However, existing level generation approaches often fail to comprehensively assess diversity across those dimensions. This paper aims to expand horizons of level diversity by considering multi-dimensional diversity when training generative models. We formulate the model training as a multi-objective learning problem, where each diversity metric is treated as a distinct objective. Furthermore, a multi-objective evolutionary learning framework that optimises multiple diversity metrics simultaneously throughout the model training process is proposed. Our case study on the commonly used benchmark Super Mario Bros. demonstrates that our proposed framework can enhance multi-dimensional diversity and identify a Pareto front of generative models, which provides a range of tradeoffs among playability and two representative diversity metrics, including a content-based one and a player-centered one. Such capability enables decision-makers to make informed choices when selecting generators accommodating a variety of scenarios and the diverse needs of players and designers.