This study develops a system to predict, with high precision and in real-time, the occurrence of difficulty
in character identification during web browsing, based on gaze data. Specifically, the system leverages
fixation duration, which evolves incrementally, and employs a reinforcement learning algorithm based
on SARSA, to evaluate the occurrence of the difficulty at each step. Since fixation durations caused by
character identification difficulty are not necessarily longer than those resulting from other factors,
establishing a reliable threshold for character magnification is difficult. Nevertheless, the system must
refrain from magnifying characters when users do not feel them difficult to identify. Therefore, this study
introduces saccadic velocity and amplitude as two external parameters, categorizes them into distinct
groups, and calculates the Q-value for each category pair, thereby enabling a precise determination of
magnification thresholds. Furthermore, a method for assigning rewards and penalties to the agent is
examined.
