Select or Suggest? Reinforcement Learning-based Method for High-Accuracy Target Selection on Touchscreens

Abstract

Suggesting multiple target candidates based on touch input is a possible option for high-accuracy target selection on small touchscreen devices. But it can become overwhelming if suggestions are triggered too often. To address this, we propose SATS, a Suggestion-based Accurate Target Selection method, where target selection is formulated as a sequential decision problem. The objective is to maximize the utility: the negative time cost for the entire target selection procedure. The SATS decision process is dictated by a policy generated using reinforcement learning. It automatically decides when to provide suggestions and when to directly select the target. Our user studies show that SATS reduced error rate and selection time over Shift [51], a magnification-based method, and MUCS, a suggestion-based alternative that optimizes the utility for the current selection. SATS also significantly reduced error rate over BayesianCommand [58], which directly selects targets based on posteriors, with only a minor increase in selection time.