Knowledge Goal Recognition for Interactive Narratives

Player goals in games are often framed in terms of achieving something in the game world, but this framing can fail to capture goals centered on the player’s own mental model, such as seeking the answers to questions about the game world. We use a least-commitment model of interactive narrative to characterize these knowledge goals and the problem of knowledge goal recognition. As a first attempt to solve the knowledge goal recognition problem, we adapt a classical goal recognition paradigm, but in our empirical evaluation the approach suffers from a high rate of incorrectly rejecting a synthetic player’s true goals; we discuss how handling of player goals could be made more robust in practice.