Simultaneous Dual Level Creation for Games.
Daniel Ashlock, Colin Lee and Cameron McGuinness
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.
Keywords: Search based procedural content generation, automatic game content generation, evolutionary computation, level
generation, dynamic programming.
EVEL generation for games is a problem within the field of Procedural Content Generation (PCG). Its goal is to
provide a map for a level in a game together with populating that level with other game content. This study extends