Dynamic and Accelerated Partial Order Planning for Interactive Narratives

Authors

  • Xun Zhang Rutgers University
  • Bhuvana Inampudi Rutgers University
  • Norman Badler University of Pennsylvania
  • Mubbasir Kapadia Rutgers University

DOI:

https://doi.org/10.1609/aiide.v13i2.12999

Keywords:

Partial order planning, Dynamic planning, Interactive narratives, Character animation, Behavior tree

Abstract

This paper explores new narrative generation paradigms for open world problems. We propose a speed-up variant of partial planner–accelerated partial order planner, that can automatically generate narratives for large plan spaces. To incorporate real-time free-form user interaction, a dynamic partial planning technique has been introduced to self-repair the narratives. We also propose a scalable and robust framework to craft open world narratives with minimal effort. Our approach enables content creators to craft complex open world narratives without explicitly authoring user interaction arcs. We tested our framework by developing multiple narratives with free-form interactions. Those narratives were used to test the robustness of the proposed planners.

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Published

2017-10-05

How to Cite

Zhang, X., Inampudi, B., Badler, N., & Kapadia, M. (2017). Dynamic and Accelerated Partial Order Planning for Interactive Narratives. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 13(2), 289–295. https://doi.org/10.1609/aiide.v13i2.12999