CRII: CHS: Fast Planning Using Computational Models of Narrative

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The objective of this research is to develop a plan-based computational model of narrative, and to use that model to automatically structure interactive experiences. When humans evaluate the importance of a narrative event, they consider not just what has happened but also events which did not happen or might have happened instead. This project will design, implement, and test a computational model of this kind of hypothetical thinking, which narrative theorists call possible worlds reasoning. This model will be used in a fast narrative planning algorithm and put to use in an existing interactive virtual environment. The results will improve our understanding of how people reason hypothetically during narrative comprehension and improve the capabilities of interactive narrative technologies to teach, train, and treat their audiences.Specifically, this research will extend previous work on hybrid planning algorithms which combine the rich knowledge representation of Partial Order Causal Link (POCL) plans with fast forward-chaining state space heuristic search. By augmenting the search space of these planners with accessibility relations the tree can be used not just as a data structure for search but also a network of possible worlds and the relationships between them. This computational model of hypothetical reasoning in narrative will be evaluated using an empirical cognitive question answering framework called QUEST. The improved algorithm, which will leverage the state-of-the-art Fast-Downward heuristic, will be evaluated in an existing interactive narrative virtual environment called The Best Laid Plans.