Journal article
Computer & Graphics, vol. 78, 2019, pp. 64-75
APA
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Padia, K., Bandara, K., & Healey, C. G. (2019). A system for generating storyline visualizations using hierarchical task network planning. Computer &Amp; Graphics, 78, 64–75.
Chicago/Turabian
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Padia, K., K. Bandara, and C. G. Healey. “A System for Generating Storyline Visualizations Using Hierarchical Task Network Planning.” Computer & Graphics 78 (2019): 64–75.
MLA
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Padia, K., et al. “A System for Generating Storyline Visualizations Using Hierarchical Task Network Planning.” Computer &Amp; Graphics, vol. 78, 2019, pp. 64–75.
BibTeX Click to copy
@article{k2019a,
title = {A system for generating storyline visualizations using hierarchical task network planning},
year = {2019},
journal = {Computer & Graphics},
pages = {64-75},
volume = {78},
author = {Padia, K. and Bandara, K. and Healey, C. G.}
}
Existing storyline visualization techniques present narratives as a node-link graph where a sequence of links shows the evolution of causal and temporal relationships between characters in the narrative. These techniques make a number of simplifying assumptions about the narrative structure, however. They assume that all narratives progress linearly in time, with a well-defined beginning, middle, and end. They assume that the narrative is complete prior to visualization. They also assume that at least two participants interact at every event. Finally, they assume that all events in the narrative occur along a single timeline. Thus, while existing techniques are suitable for visualizing linear narratives, they are not well suited for visualizing narratives with multiple timelines, non-linear narratives such as those with flashbacks, or for narratives that contain events with only one participant. In our previous work we presented Yarn, a system for automatic construction and visualization of narratives with multiple timelines. Yarn employs hierarchical task network planning to generate all possible narrative timelines and visualize them in a web-based interface. In this work, we extend Yarn to support non-linear narratives with flash-backs and flash-forwards, and non-linear point-of-view narratives. Our technique supports both single- participant as well as multi-participant events in the narrative, and constructs both linear as well as non-linear narratives. Additionally, it enables pairwise comparison within a group of multiple narrative timelines.