Extending neuro-evolutionary preference learning through player modeling

In this paper we propose a methodology for improving the accuracy of models that predict self-reported player pairwise preferences. Our approach extends neuro-evolutionary preference learning by embedding a player modeling module for the prediction of player preferences. Player types are identified using self-organization and feed the preference learner. Our experiments on a dataset derived from a game survey of subjects playing a 3D prey/predator game demonstrate that the player model-driven preference learning approach proposed improves the performance of preference learning significantly and shows promise for the construction of more accurate cognitive and affective models.