Story generation is the problem of automatically selecting a sequence of events that meet a set of criteria and can be told as a story. Story generation is knowledge-intensive; traditional story generators rely on a priori defined domain models about fictional worlds, including characters, places, and actions that can be performed. Manually authoring the domain models is costly and thus not scalable.
We present a novel class of story generation system--called an Open Story Generator--that can generate stories about any topic. Our system, Scheherazade, generates plausible-sounding, but fictional stories about real-world situations. It automatically learns a domain model by crowdsourcing a corpus of narrative examples and generates stories by sampling from the space defined by the domain model.
Scheherazade can also be used to create interactive narratives in which a player gets to choose the actions for a particular character in the crowdsourced story world. See a video of the system in action: https://www.youtube.com/v/znqw17aOrCs
The Entertainment Intelligence Lab focuses on computational approaches to creating engaging and entertaining experiences. Some of the problem domains they work on include, computer games, storytelling, interactive digital worlds, adaptive media and procedural content generation. They expressly focus on computationally "hard" problems that require automation, just-in-time generation, and scalability of personalized experiences.