Interactive narratives (IN) are stories that branch and change based on the actions of a participant. A class of automated systems generate INs where all story branches conform to a set of constraints predefined by an author. Participants in these systems may create invalid branches by navigating the story world outside the bounds of an author's constraints. We approach this problem from an adversarial game perspective, where the IN system's goal is to prevent the player from creating invalid branches. From this perspective, one way an IN system can take action is to transition the game world between alternate states that are consistent with the player's observations during gameplay. In this paper we present a method of modelling and updating sets of world states consistent with player knowledge as a single superposed data structure. We discuss how this data structure can be used in an IN framework to maximize the probability that author constraints are maintained during gameplay.