Simultaneous Dual Level Creation for Games

Daniel Ashlock, Colin Lee, and Cameron McGuinness
Submitted to Computational Intelligence Magazine

Abstract PDF eprint

Recent research has shown that it is possible to design fitness functions, based on dynamic programming, that allow evolutionary computation to automatically generate level maps for games. In this study levels with multiple types of barriers are automatically designed. The levels are designed under the assumption that there are two agent types and that at least one agent type may ignore one type of barrier. A specification of multiple types of barriers thus creates two mazes, one for each agent type, that co-exist in the same space. The design of these dual mazes is accomplished using different fitness functions for two mazes simultaneously. This permits, for example, a level with a single long winding path for an agent that cannot walk through one type of barrier co-existing with a low-diameter maze with more complex connectivity for an agent that cannot cross another type of barrier. This study explores two representations for game levels with multiple barrier types using four different pairs of fitness functions. The system is shown to be able to design dual mazes whose properties depend substantially on both the choice of fitness function and representation used.