  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.
