Using Active Probing by a Game Management AI to Faster Classify Players

In this paper, we present the use of a so-called Game Management AI to classify players not just by passively observing them, but by actively manipulating the game to get the players to provide data currently missing to achieve the classification. We call this "Active Probing". The Game Management AI uses two sets of rules, one set that contains rules that are intended to represent the knowledge allowing a classification and one set that contains rules that indicate which game events can contribute to triggering conditions used in the first rule set. When a rule of the first set comes near to being triggered, the event suggested by an appropriate rule in the second set is then offered to the player in the game. We instantiated this use of a Game Management AI to identify players with a very high interest level for the role playing game "Realm of Dreams", a game that we created for this purpose. Our experimental evaluation showed that using the active probing by the Game Management AI allows us to identify players in our targeted class in a quarter of the time that was needed to classify such players without active probing.