Optimization of Platform Game Levels for Player Experience

Abstract

We demonstrate an approach to modelling the effects of certain parameters of platform game levels on the players' experience of the game. A version of Super Mario Bros has been adapted for generation of parameterized levels, and experiments are conducted over the web to collect data on the relationship between level design parameters and aspects of player experience. These relationships have been learned using preference learning of neural networks. The acquired models will form the basis for artificial evolution of game levels that elicit desired player emotions.