Multi-Objective level generator generation with Marahel
This addresses the need for automated level generation in game design, but it is incremental as it builds on existing Marahel language and optimization methods.
The paper tackles the problem of automatically designing constructive level generators for games by searching the space of generators defined by the Marahel language using NSGA-II, achieving good performance on fitness functions for Binary, Zelda, and Sokoban problems, though with limitations in Zelda and Sokoban where generators rely on initial states rather than map modifications.
This paper introduces a new system to design constructive level generators by searching the space of constructive level generators defined by Marahel language. We use NSGA-II, a multi-objective optimization algorithm, to search for generators for three different problems (Binary, Zelda, and Sokoban). We restrict the representation to a subset of Marahel language to push the evolution to find more efficient generators. The results show that the generated generators were able to achieve good performance on most of the fitness functions over these three problems. However, on Zelda and Sokoban, they tend to depend on the initial state than modifying the map.