Learning to Engage in Interactive Digital Art

The aim of this study was to determine whether reinforcement learning could increase user engagement in interactive art installations. Building on a physical interactive art installation called The Plants [1] by Playable Streets, reinforcement learning was integrated into a web application adapted from the physical interactive art piece. The original installation consisted of real plants that visitors could touch to produce sounds. A digital model and interface was developed to simulate the physical installation. A user study was conducted with 178 participants. Three modes were examined: the original settings of the installation as designed by the artist; a predetermined fixed schedule of consistently changing sound banks; and a reinforcement learning mode, where an agent changes the interactive behaviours to maximise user engagement. User engagement was estimated by comparing the number of touches by an individual over successive time intervals.

From the trial, it was found that reinforcement learning was able to improve average engagement levels of users by nearly 27%. However, reinforcement learning was not able to increase the average duration users interacted with the installation.