Dynamic Difficulty Adjustment Using Performance and Affective Data in a Platform Game

The Dynamic Difficulty Adjustment (DDA) of games can play an important role in increasing the player engagement and fun. Gameplay difficulty can be adapted according to the player’s performance, its affective state or by using a hybrid model that combines both approaches. This work investigates a hybrid DDA mechanism for a platform game to appropriately adapt its difficulty level and keep the player in a state of flow. The three approaches are compared to verify the efficiency of each model. An open source platform game was adapted to support the hybrid DDA algorithms. Game telemetry was introduced to acquire performance data and the affective state of the player is estimated through physiological data obtained from the Electrodermal Activity (EDA) of the skin. A method that estimates game difficulty when varying platform size and jump height was developed to support the DDA process. Besides playing with the different DDA models, each participant answered questionnaires and had their data collected for inquiry purposes. The results indicate that the DDA models were able to adjust the gameplay difficulty to the players, increasing the number of completed levels and reducing the variation in the playing time.