arXiv:2404.03054v1 Announce Type: new
Abstract: 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.
Goal recognition design is an important area of research that focuses on modifying decision-making environments to make it easier to infer the goals of agents within those environments. While previous approaches have focused on computationally demanding methods and assumed near-optimal decision-making, this article introduces a data-driven approach that can account for agents with general behavioral models.
The authors use worst-case distinctiveness (wcd) as a measure of the difficulty in inferring an agent’s goal. They train a machine learning model to predict wcd for a given environment and agent behavior model. To optimize decision-making environments for enhanced goal recognition, they propose a gradient-based optimization framework that can handle various constraints.
The multi-disciplinary nature of this research is evident in its combination of machine learning and decision-making theory. By integrating these fields, the authors are able to create a framework that not only outperforms existing methods but also adapts to scenarios not previously covered in the literature.
The article also highlights the practical implications of this research through human-subject experiments. These experiments confirm that the method proposed by the authors can create environments that facilitate efficient goal recognition from real-world human decision-makers.
This research has significant implications for a wide range of domains. In fields such as autonomous systems, robotics, and human-computer interaction, the ability to accurately infer an agent’s goals is crucial. By providing a more efficient and adaptable approach to goal recognition design, this research opens up new possibilities for enhancing decision-making processes in these domains.