Towards Challenge Balancing for Personalised Game Spaces

This article focuses on games that can tailor the provided game experience to the individual player (personalised games), typically by effectively utilising player models. A particular challenge in this regard, is utilising player models for assessing online (i.e., while the game is being played) and unobtrusively which game adaptations are appropriate. In this article, we propose an approach for personalising the space in which a game is played (i.e., levels) - to the end of tailoring the experienced challenge to the individual player during actual play of the game. Our approach specically
considers two persisting design challenges, namely implicit user feedback and high risk of user abandonment. Our contribution to this end is proposing a clear separation between (intelligent) offine exploration and (safety-conscious) online exploitation. We are presently assessing the effectiveness of the developed approach in an actual video game: Infinite Mario Bros. To this end, we have enhanced the game such that its process for procedural-content generation allows the game spaces (i.e., levels) to be personalised during play of the game. We use data from intelligent oine exploration to determine both a model of experienced challenge as well as safe level design parameters for use on new players. Online, we use a gradient ascent algorithm with designer-specified domain knowledge to select the next set of level design parameters.