# PlotThread: Creating Expressive Storyline Visualizations using Reinforcement Learning

arXiv: https://arxiv.org/abs/2009.00249 · HTML: https://mlsanity.com/paper/2009.00249/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [Chart Question Answering: State of the Art and Future Directions](https://mlsanity.com/paper/2205.03966/) (2022)
- [Generative AI for Visualization: State of the Art and Future Directions](https://mlsanity.com/paper/2404.18144/) (2024)
- [Reinforced Labels: Multi-Agent Deep Reinforcement Learning for Point-Feature Label Placement](https://mlsanity.com/paper/2303.01388/) (2023)
- [Adversarial Attacks on Machine Learning-Aided Visualizations](https://mlsanity.com/paper/2409.02485/) (2024)
- [Versatile Ordering Network: An Attention-based Neural Network for Ordering Across Scales and Quality Metrics](https://mlsanity.com/paper/2412.12759/) (2024)
- [Story Ribbons: Reimagining Storyline Visualizations with Large Language Models](https://mlsanity.com/paper/2508.06772/) (2025)
- [Feature-Action Design Patterns for Storytelling Visualizations with Time Series Data](https://mlsanity.com/paper/2402.03116/) (2024)

## Abstract (arXiv)

Storyline visualizations are an effective means to present the evolution of plots and reveal the scenic interactions among characters. However, the design of storyline visualizations is a difficult task as users need to balance between aesthetic goals and narrative constraints. Despite that the optimization-based methods have been improved significantly in terms of producing aesthetic and legible layouts, the existing (semi-) automatic methods are still limited regarding 1) efficient exploration of the storyline design space and 2) flexible customization of storyline layouts. In this work, we propose a reinforcement learning framework to train an AI agent that assists users in exploring the design space efficiently and generating well-optimized storylines. Based on the framework, we introduce PlotThread, an authoring tool that integrates a set of flexible interactions to support easy customization of storyline visualizations. To seamlessly integrate the AI agent into the authoring process, we employ a mixed-initiative approach where both the agent and designers work on the same canvas to boost the collaborative design of storylines. We evaluate the reinforcement learning model through qualitative and quantitative experiments and demonstrate the usage of PlotThread using a collection of use cases.