Jacob, ChristianDenzinger, JorgEidelberg, Arkady2021-07-082021-07-082021-06Eidelberg, A. (2021). Using active probing by a Game Management AI to faster classify players in online video games (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.http://hdl.handle.net/1880/113602A Game Management AI is a framework to classify players based on their interest of the game. It is different from other work in this area by the fact that it actively manipulates the game state. This encourages the players to act in a certain way (or not), indirectly providing data currently missing to achieve the classification. This is called “Active Probing". The Game Management AI uses two sets of rules. The first contains rules that are intended to represent the knowledge allowing a classification and the second contains rules that indicate which game events can contribute to triggering conditions used in the first rule set. The Game Management AI was evaluated on the role playing game “Realm of Dreams”, a game that was created for this purpose. The experimental evaluation showed that using the active probing by the Game Management AI allows dentification of players highly interested in the game four times faster than such players were identified without active probing.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Artificial IntelligenceGame Management AIVideo GamesActive ProbingPlayer MotivationComputer Sciencemaster thesis10.11575/PRISM/38991