The presentation and poster was part of my fourth year project in Information Technology Engineering, Artificial Intelligence Department, Damascus, Syria. This research study take games development concept to a new level, especially the so called First Person Shooter (FPS) Games. This study outline three basic models: FPS Game Level Design and Procedural Content Generation for FPS games, Preference Learning and Adaptive Content Generation. The framework has been integrated with CUBE opensource game engine. A conference paper has been published in UMAP 2013 which you can find in the publication section (with Noor Shaker, Mehdi Zonji, Ismaeel Abu Abdalla and Mhd Hasan Sarhan.) In this paper (abstract), we describe a methodology for capturing player experience while interacting with a game and we present a data-driven approach for modelling this interaction. We believe the best way to adapt games to a specific player is to use quantitative models of player experience derived from the in-game interaction. Therefore, we rely on crowd-sourced data collected about game context, players behaviour and players self-reports of different affective states. Based on this information, we construct estimators of player experience using neuroevolutionary preference learning. We present the experimental setup and the results obtained from a recent case study where accurate estimators were constructed based on information collected from players playing a first 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. Authors are:  Noor Shaker, Mohammad Shaker, Ismaeel Abuabdallah, Mehdi Zonjy, and Mhd Hasan Sarhan.

The poster we presented in the Extended Proceedings of the 2013 Conference on User Modeling, Adaptation and Persolization (UMAP 2013), 2013.

You can download the full project documentation [in Arabic – بالعربية] here.