Data-Driven Goal Recognition Design for General Behavioral Agents

2024-04-03

arXiv.org

AI Summary

Existing target identification design methods incur high computational overhead and critically rely on the assumption of optimal surrogate decision-making, rendering them ill-suited to real-world suboptimal human behavior and complex environments. To address this, we propose a data-driven framework compatible with general behavioral models, which— for the first time—integrates machine learning with constraint-aware gradient optimization to enable adaptive design of decision environments. Our approach constructs a differentiable predictive model based on the Worst-Case Deviation (WCD) metric, supporting flexible resource budgets and explicit modeling of non-optimal strategies. Simulations demonstrate significant WCD reduction and improved runtime efficiency. Human-subject experiments further confirm that our method effectively guides real decision-makers toward faster and more accurate target identification. By relaxing the restrictive optimality assumption, this work extends the applicability of target identification design to practical human–machine collaborative settings.

Technology Category

• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Search and Optimization: Adversarial Search

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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($ extit{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 $ extit{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 $ extit{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.

Problem

• Improving goal recognition efficiency using machine learning methods
• Optimizing environments to infer goals of general behavioral agents
• Reducing worst-case distinctiveness under various practical constraints

Innovation

• Machine learning predicts worst-case distinctiveness for environments
• Gradient-based optimization framework accommodates various constraints
• Method adapts to flexible budgets and suboptimal agent behavior