Empirical evaluation of procedural level generators for 2D platform games

Context. Procedural content generation (PCG) refers to algorithmical creation of game content (e.g. levels, maps, characters). Since PCG generators are able to produce huge amounts of game content, it becomes impractical for humans to evaluate them manually. Thus it is desirable to automate the process of evaluation. Objectives. This work presents an automatic method for evaluation of procedural level generators for 2D platform games. The method was used for comparative evaluation of four procedural level generators developed within the research community. Methods. The evaluation method relies on simulation of the human player's behaviour in a 2D platform game environment. It is made up of three components: (1) a 2D platform game Infinite Mario Bros with levels generated by the compared generators, (2) a human-like bot and (3) quantitative models of player experience. The bot plays the levels and collects the data which are input to the models. The generators are evaluated based on the values output by the models. A method based on the simple moving average (SMA) is suggested for testing if the number of performed simulations is sufficient. Results. The bot played all 6000 evaluated levels in less than ten minutes. The method based on the SMA showed that the number of simulations was sufficiently large. Conclusions. It has been shown that the automatic method is much more efficient than the traditional evaluation made by humans while being consistent with human assessments