An Intent-Driven Planner for Multi-Agent Story Generation
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U-director: a decision-theoretic narrative planning architecture for storytelling environments
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Narratives that prompt inferences can be more interesting in that they provide the reader with the opportunity to reason about the narrative world, participating in its construction. These narratives can also be more concise and direct, as details can be filled in by the reader. On the other hand, narratives that leave out important information without the opportunity to infer this information may be incoherent. To generate narratives that prompt inferences a system must 1) employ a theory of how inferences are prompted and 2) provide a capacity for creating narratives that satisfy inference goals. This paper presents is a novel algorithm for generating discourse plans that prompt inferences according to a theory of online inferencing in narrative discourse. Though other approaches have generated narrative and discourse structures to influence the reader's perception of the narrative, this is the first approach to present an empirically based cognitive model of online inference generation. The algorithm is a partial-order planning approach to discourse generation, selecting events to tell the reader from an input story plan.