Computational Intelligence-based Entertaining Level Generation for Platform Games

With computers becoming ubiquitous and high resolution graphics reaching the next level, computer games have become a major source of entertainment. It has been a tedious task for game developers to measure the entertainment value of the computer games. The entertainment value of a game does depend upon the genre of the game in addition to the game contents. In this paper, we propose a set of entertainment metrics for the platform genre of games. The set of entertainment metrics is proposed based upon certain theories on entertainment in computer games. To test the metrics, we use an evolutionary algorithm for automated generation of game rules which are entertaining. The proposed approach starts with an initial set of randomly generated games and, based upon the proposed metrics as an objective function, guides the evolutionary process. The results produced are counterchecked against the entertainment criteria of humans by conducting a human user survey and a controller learning ability experiment. The proposed metrics and the evolutionary process of generating games can be employed by any platform game for the purpose of automatic generation of interesting games provided an initial search space is given.