Learning Player Preferences to Inform Delayed Authoring

Of all forms of Intelligent Narrative, interactive narratives are uniquely well-poised to benefit from player modelling techniques. Given the availability of immediate player feedback as interactions with the narrative’s world, the traditional task of delayed authoring can be informed with the author’s knowledge of which types of players should prefer each event; this allows generated narratives to dynamically adapt and fulfill the player preferences collected by the model. In this paper, we present PaSSAGE (Player-Specific Stories via Automatically Generated Events), our implementation of preference-informed delayed authoring in the setting of interactive entertainment. Recent results from a human user study with 102 participants indicate that for players with minimal previous gaming experience who found the game easy to follow, using our preference-informed techniques can improve their enjoyment of a computer role-playing game.