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 in an unknown domain. Our system, Scheherazade, (a) automatically learns a domain model by crowdsourcing a corpus of narrative examples and (b) 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. For more on how automatically generated interactive narratives can be used for training and education see the DARPA Press Release.
Brent Harrison and Mark O. Riedl. Towards Learning From Stories: An Approach for Interactive Machine Learning. Proceedings of the AAAI Workshop on Symbiotic Cognitive Systems, Phoenix, Arizona, 2016.
Matthew Guzdial, Brent Harrison, Boyang Li, and Mark O. Riedl. Crowdsourcing Open Interactive Narrative. Proceedings of the 10th International Conference on the Foundations of Digital Games, Asilomar, California, 2015.
Boyang Li. Learning Knowledge to Support Domain-Independent Narrative Intelligence. Ph.D. Dissertation, Georgia Institute of Technology.
Boyang Li, Mohini Thakkar, Yijie Wang, and Mark O. Riedl. Storytelling with Adjustable Narrator Styles and Sentiments. Proceedings of the 2014 International Conference on Interactive Digital Storytelling, Singapore, 2014.
Rania Hodhod, Marc Huet, and Mark O. Riedl. Toward Generating 3D Games with the Help of Commonsense Knowledge and the Crowd. Proceedings of the AAAI Workshop on Experimental AI in Games, Raleigh, NC, 2014.
Boyang Li, Mohini Thakkar, Yijie Wang, and Mark O. Riedl. Data-Driven Alibi Story Telling for Social Believability. Proceedings of the 2014 Foundations of Digital Games Workshop on Social Behavior in Games, Ft. Lauderdale, Florida, 2014.
Boyang Li, Stephen Lee-Urban, and Mark O. Riedl. Crowdsourcing interactive fiction games. Proceedings of the 8th International Conference on the Foundations of Digital Games, Chania, Crete, Greece, 2013.
Boyang Li, Stephen Lee-Urban, George Johnston, and Mark O. Riedl. Story Generation with Crowdsourced Plot Graphs. Proceedings of the 27th AAAI Conference on Artificial Intelligence, Bellevue, Washington, 2013.
Boyang Li, Stephen Lee-Urban, D. Scott Appling, and Mark O. Riedl. Crowdsourcing Narrative Intelligence. Advances in Cognitive Systems, vol. 2, 2012.
Boyang Li. Narrative Intelligence Without (Domain) Boundaries. Proceedings of the 8th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Doctoral Consortium, Palo Alto, California, 2012.
Boyang Li, Stephen Lee-Urban, and Mark O. Riedl. Toward Autonomous Crowd-Powered Creation of Interactive Narratives. Proceedings of the 5th AAAI Workshop on Intelligent Narrative Technologies, Palo Alto California, 2012.
Boyang Li, D. Scott Appling, Stephen Lee-Urban, and Mark O. Riedl. Learning Sociocultural Knowledge via Crowdsourced Examples. Proceedings of the 4th AAAI Workshop on Human Computation, Toronto, Canada, 2012.
Boyang Li, Stephen Lee-Urban, Darren Scott Appling, and Mark O. Riedl. Automatically Learning to Tell Stories about Social Situations from the Crowd. Proceedings of the LREC 2012 Workshop on Computational Models of Narrative, Istanbul, Turkey, 2012.