Extending a MAPE-K loop-based framework for Dynamic Difficulty Adjustment in single-player games

Abstract

Dynamic Difficulty Adjustment (DDA) has emerged as a notable solution to address the demand for adaptive gameplay in digital games. However, the DDA domain presents various research challenges that require careful consideration. In response, this study introduces an approach that merges insights from self-adaptive systems with the specific requirements of adaptive gameplay. Our contribution involves extending the DDA-MAPEKit framework, which was previously presented. Constructed on the modular MAPE-K loop foundation, this solution facilitates the assimilation of multiple DDA strategies. The primary objective is to provide tailored treatment for distinct game mechanics (or groups of mechanics) by constructing individual MAPE-K loops for each of them. Notable enhancements include the development of a descriptive player model involving continuous player profile assessment, performance calculation, and modifications to the rules’ system to better align with the player’s conditions. A proof of concept is executed by applying DDA-MAPEKit in a Space Shooter game to assess the viability of the proposed model. Simulations are conducted both with and without the DDA mechanisms. Encouraging results are obtained through a comparative analysis of the gathered data. Evidence points out that incorporating the DDA mechanism, implemented with DDA-MAPEKit, may effectively lead to adapting variables depicting the complexity of game mechanics following the player’s performance. This fact underscores the potential effectiveness of our approach in addressing the challenges within the realm of DDA.