  A chart sequence is used to describe a series of visualization charts
generated in the exploratory analysis by data analysts. It provides information
details in each chart as well as a logical relationship among charts. While
existing research targets on generating chart sequences that match human's
perceptions, little attention has been paid to formulate task-oriented
connections between charts in a chart design space. We present a novel chart
sequencing method based on reinforcement learning to capture the connections
between charts in the context of three major analysis tasks, including
correlation analysis, anomaly detection, and cluster analysis. The proposed
method formulates a chart sequencing procedure as an optimization problem,
which seeks an optimal policy to sequencing charts for the specific analysis
task. In our method, a novel reward function is introduced, which takes both
the analysis task and the factor of human cognition into consideration. We
conducted one case study and two user studies to evaluate the effectiveness of
our method under the application scenarios of visualization demonstration,
sequencing charts for reasoning analysis results, and making a chart design
choice. The study results showed the power of our method.
