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
setups, and it also adapts to scenarios not previously covered in the
literature, such as those involving flexible budget constraints, more complex
environments, and suboptimal agent behavior. Moreover, 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.

目标识别设计旨在在决策环境中作出有限的修改，以使更容易推测在该环境下行动的代理人的目标。我们通过使用数据驱动的方法和基于梯度的优化框架来对目标识别设计进行优化，以解决现有方法中的计算需求和对代理人决策的假设性限制，从而实现对决策环境的改进。