Computer Aided Content Generation – A Gloomhaven Case Study
Marcus Gerhold, Kristian Tijben · 2023
We present how an evolutionary algorithm can be used to generate scenarios for the board game Gloomhaven. The scenarios are evaluated according to size, difficulty, thematic coherence, complexity and layout. We encode the game’s default scenarios into textual descriptions and use them as initial population for the algorithm. Our dungeon generation works within the confines given by the physical board game, i.e., special attention is given to availability of game pieces and map tiles. The generated dungeons can be constructed without overlapping tiles.