Browsing by Author "Eidelberg, Arkady"

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    Using Active Probing by a Game Management AI to Faster Classify Players in Online Video Games
    (2021-06) Eidelberg, Arkady; Jacob, Christian; Denzinger, Jorg; Aycock, John Daniel; Zhao, Richard
    A 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.