learning from preferences and selected multimodal features of players
The influence of multimodal sources of input data to the construction of accurate computational models of user preferences is investigated in this paper. The case study presented explores player entertainment preferences of physical game variants incorporating two data modalities. The main findings of the paper reveal the benefit of multiple modalities of input data for the prediction of preferences and highlight the impact of feature selection on the construction of such models.
evolving artificial neural networks preference learning player satisfaction modeling augmented reality games