Empowering Quality Diversity in Dungeon Design with Interactive Constrained MAP-Elites
This work addresses the challenge of enhancing creativity and flexibility in game level design for developers and designers, though it appears incremental as it builds on existing MAP-Elites and mixed-initiative approaches.
The paper tackled the problem of mixed-initiative game content generation by applying quality-diversity algorithms, specifically MAP-Elites, to dungeon design, resulting in a system that allows users to dynamically choose variation dimensions and incorporate algorithmic suggestions into their designs.
We propose the use of quality-diversity algorithms for mixed-initiative game content generation. This idea is implemented as a new feature of the Evolutionary Dungeon Designer, a system for mixed-initiative design of the type of levels you typically find in computer role playing games. The feature uses the MAP-Elites algorithm, an illumination algorithm which divides the population into a number of cells depending on their values along several behavioral dimensions. Users can flexibly and dynamically choose relevant dimensions of variation, and incorporate suggestions produced by the algorithm in their map designs. At the same time, any modifications performed by the human feed back into MAP-Elites, and are used to generate further suggestions.