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There has been much research into making planning-based story generators more efficient; however, the question remains whether the same efficiency could be achieved by reducing the problem to a more widely-studied search problem and leveraging existing solvers. We investigate this question for the narrative planning formalism used by Sabre, which models character goals and beliefs with deeply-nested theory of mind. We use answer set programming to develop a declarative implementation of the same planning formalism. Benchmarking our implementation, we find that existing, specialized planners remain the state of the art for solving their target problems as quickly as possible. However, the compactness and modularity of our approach will make it easier for researchers to develop prototype generators for new solution spaces that build on existing models. 

A narrative planner decides the actions for all characters in a story while justifying each action according to the acting characters' own individual goals. However, an action that contributes to a goal may still seem irrational when considered alongside other actions the character may take; for instance, it may sacrifice a more important goal, or expose the character to unnecessary risks. We redefine the narrative planning character model to use a multiobjective framework where character actions are chosen from a Pareto front of best and safest options. We discuss how this framework can be applied to generate a policy for how the non-player characters should behave in any given state of an interactive narrative, and how we applied such a policy in the Traffic Stop de-escalation training simulation. 

Supporting high-agency player experiences without compromising narrative control is one of the major challenges in digital interactive narrative design. Humans, on the other hand, frequently meet this challenge when cooperating to improvise a narrative. We present a study examining how humans improvise narratives when paired together as the player and game master of a digital interactive narrative. We collected gameplay logs from these experiences, as well as participants’ reported perceptions of narrative structure, personal agency, and the reasons for both their choices and their partners’. We found a strong link between perceptions of structure and of agency. We also found a tendency for participants to better identify the goals of their partner’s actions following sessions where game masters expressed higher agency. Finally, we characterize the experiences using principles of improv theatre, drawing from the data to analyze negative experiences of agency as failures in the improv partnership. 

We discuss the ongoing development of MicroTales, a collection of elements that can be combined to generate interactive narrative environments of varying size and complexity. MicroTales aims to fill a gap among existing AI benchmarks by featuring active non-player characters, a wide variety of actions, and the possibility to soft-lock the problem. Its purpose is to provide a clearly defined environment to compare different experience management algorithms without enforcing any one definition of what makes a good story. We present design goals and a sketch of an initial design, and invite community feedback to help make our benchmark reusable by other narrative intelligence researchers. 

Traffic Stop is a virtual reality de-escalation training simulation for police officers that has an interactive story driven by artificial intelligence. This project was funded by the U.S. National Science Foundation and led by Prof. Stephen Ware of the Narrative Intelligence Lab at the University of Kentucky.