On balance and dynamism in procedural content generation with self-adaptive evolutionary algorithms

Abstract

We consider search-based procedural content generation in the context of Planet Wars, an RTS game. The objective of this work is to generate maps for the aforementioned game, that result in an interesting game-play. In order to characterize interestingness we focus on the properties of balance and dynamism. The former captures the fact that no player is overwhelmed by the opponent during the game, whereas the latter tries to model the fact that there is a lot of action during the game. To measure these properties on a given map, we conduct several games on them using top AI bots and collect statistics which are, in turn, used as inputs of a fuzzy rule base. This system is embedded within an evolutionary algorithm that features self-adaptation of mutation parameters as well as variable-length chromosomes (thus implying maps of different sizes). The experimentation focuses both on the optimization of balance and dynamism as stand-alone properties and in the analysis of the different tradeoffs attainable through them. To reach this goal a multi objective approach is used. We analyze both the usefulness of map-size self-adaptation in each scenario, as well as the properties of maps leading to different tradeoffs between dynamism and balance.