Probabilistic Goal Recognition in Interactive Narrative Environments

Bradford Mott, Sunyoung Lee, James Lester

Recent years have witnessed a growing interest in interactive narrative-centered virtual environments for education, training, and entertainment. Narrative environments dynamically craft engaging story-based experiences for users, who are themselves active participants in unfolding stories. A key challenge posed by interactive narrative is recognizing users' goals so that narrative planners can dynamically orchestrate plot elements and character actions to create rich, customized stories. In this paper we present an inductive approach to predicting users' goals by learning probabilistic goal recognition models. This approach has been evaluated in a narrative environment for the domain of microbiology in which the user plays the role of a medical detective solving a science mystery. An empirical evaluation of goal recognition based on n-gram models and Bayesian networks suggests that the models offer significant predictive power.

Subjects: 6. Computer-Human Interaction


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