  Goal recognition design aims to make limited modifications to decision-making
environments with the goal of making it easier to infer the goals of agents
acting within those environments. Although various research efforts have been
made in goal recognition design, existing approaches are computationally
demanding and often assume that agents are (near-)optimal in their
decision-making. To address these limitations, we introduce a data-driven
approach to goal recognition design that can account for agents with general
behavioral models. Following existing literature, we use worst-case
distinctiveness($\textit{wcd}$) as a measure of the difficulty in inferring the
goal of an agent in a decision-making environment. Our approach begins by
training a machine learning model to predict the $\textit{wcd}$ for a given
environment and the agent behavior model. We then propose a gradient-based
optimization framework that accommodates various constraints to optimize
decision-making environments for enhanced goal recognition. Through extensive
simulations, we demonstrate that our approach outperforms existing methods in
reducing $\textit{wcd}$ and enhancing runtime efficiency in conventional setup.
Moreover, our approach also adapts to settings in which existing approaches do
not apply, such as those involving flexible budget constraints, more complex
environments, and suboptimal agent behavior. Finally, we have conducted
human-subject experiments which confirm that our method can create environments
that facilitate efficient goal recognition from real-world human
decision-makers.
