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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.
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Generating Explorable Narrative Spaces with Answer Set Programming
Previous approaches to narrative generation have required a new planner implementation for each set of constraints deemed relevant to the narrative domain, each consisting of thousands of lines of code and supporting one primary mode of interaction: fully specifying a domain and problem, and receiving a plan as output. We present a lightweight, flexible narrative planner written with Answer Set Programming, designed specifically to support constraint-based narrative generation, show how it generalizes previous approaches, and show how it can be easily extended with notions of thematic plot schema such as “betrayal.” Finally, we demonstrate how the ASP model can be explored through interactive question answering, where answers take the form of generated narratives. In the long term, we intend this work to support understanding of complex rule systems through interactive exploration.
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- Award ID(s):
- 1846122
- PAR ID:
- 10233629
- Editor(s):
- Lelis, Levi; Thue, David
- Date Published:
- Journal Name:
- Sixteenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
- Volume:
- 16
- Issue:
- 1
- ISSN:
- 2334-0924
- Page Range / eLocation ID:
- 45-51
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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