Abstract: "In this paper, we describe a methodology for capturing player
experience while interacting with a game and we present a data-driven
approach for modeling this interaction. We believe the best way to adapt
games to a specic player is to use quantitative models of player ex-
perience derived from the in-game interaction. Therefore, we rely on
crowd-sourced data collected about game context, players behavior and
players self-reports of dierent aective states. Based on this informa-
tion, we construct estimators of player experience using neuroevolution-
ary preference learning. We present the experimental setup and the re-
sults obtained from a recent case study where accurate estimators were
constructed based on information collected from players playing a rst-
person shooter game. The framework presented is part of a bigger picture
where the generated models are utilized to tailor content generation to
particular player's needs and playing characteristics."