PlotThread: Creating Expressive Storyline Visualizations using Reinforcement Learning

TL;DR

This paper introduces PlotThread, a reinforcement learning-based tool that helps users efficiently create and customize storyline visualizations by exploring design options collaboratively with an AI agent.

Contribution

It presents a novel reinforcement learning framework and an interactive authoring tool for flexible, efficient storyline visualization design and customization.

Findings

Reinforcement learning effectively explores storyline design space.

PlotThread enables flexible customization of storyline visualizations.

The tool improves collaboration between users and AI in visualization design.

Abstract

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.