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Planning-based narrative generation is effective at producing stories with a logically-sound flow of events, but it can be limiting due to the rigidity of its constraints and the high burden on the domain author to define story-world objects, initial states, and author and character goals. Giving the system the freedom to add objects and events to the story-world history arbitrarily can improve variety and reduce authorial burden, but risks leading to stories that seem jarringly contrived to the audience. I propose to use question-answering as the antidote to contrivance in a highly-generative interactive narrative system: By modeling the player's beliefs about the story world, inferring the implicit questions the player may be asking through their interactions, and answering those questions in a way consistent with the player's prior knowledge, a system could focus on creating cohesion in the ways that matter most to the player while accepting a degree of contrivance in the details that the player is likely to overlook.
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Salience Vectors for Measuring Distance Between Stories
Narrative planners generate sequences of actions that represent story plots given a story domain model. This is a useful way to create branching stories for interactive narrative systems that maintain logical consistency across multiple storylines with different content. There is a need for story comparison techniques that can enable systems like experience managers and domain authoring tools to reason about similarities and differences between multiple stories or branches. We present an algorithm for summarizing narrative plans as numeric vectors based on a cognitive model of human story perception. The vectors encode important story information and can be compared using standard distance functions to quantify the overall semantic difference between two stories. We show that this distance metric is highly accurate based on human annotations of story similarity, and compare it to several alternative approaches. We also explore variations of our method in an attempt to broaden its applicability to other types of story systems.
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- Award ID(s):
- 1911053
- PAR ID:
- 10374337
- Date Published:
- Journal Name:
- Proceedings of the 18th AAAI international conference on Artificial Intelligence and Interactive Digital Entertainment
- Page Range / eLocation ID:
- 95-104
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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