User Agency and System Automation in Interactive Intelligent Systems

2025-02-19

AI Summary

How to dynamically balance user autonomy and system automation in intelligent human–machine interaction? This paper proposes a novel human–machine collaboration paradigm. It employs a spherical electromagnetic actuator to deliver cable-free, tunable magnetic haptic feedback; integrates markerless passive magnetic tool tracking with closed-loop control to enable implicit human behavioral modeling within an optimal shared control framework; and further introduces a model-agnostic deep reinforcement learning–based adaptive interface that enables online optimization without requiring real-user data. The co-design of algorithm, hardware, and interface significantly enhances interaction intuitiveness and efficiency. User studies and simulations demonstrate that the proposed shared control strategy outperforms both fully autonomous and fully manual baselines in task performance and alignment with user intent—validating the efficacy and generality of the dynamic authority-allocation mechanism.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Interaction Techniques and Devices
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: The sharing economy

Abstract

Balancing user agency and system automation is essential for effective human-AI interactions. Fully automated systems can deliver efficiency but risk undermining usability and user autonomy, while purely manual tools are often inefficient and fail to enhance user capabilities. This dissertation addresses the question:"How can we balance user agency and system automation for interactions with intelligent systems?"We present four main contributions. First, we develop a spherical electromagnet that provides adjustable forces on an untethered tool, allowing haptic feedback while preserving user agency. Second, we create an integrated sensing and actuation system that tracks a passive magnetic tool in 3D and delivers haptic feedback without external tracking. Third, we propose an optimal control method for electromagnetic haptic guidance that balances user input with system control, enabling users to adjust trajectories and speed. Finally, we introduce a model-free reinforcement learning approach for adaptive interfaces that learns interface adaptations without heuristics or real user data. Our simulations and user studies show that shared control significantly outperforms naive strategies. By incorporating explicit or implicit models of human behavior into control strategies, intelligent systems can better account for user agency. We demonstrate that the trade-off between agency and automation is both an algorithmic challenge and an engineering concern, shaped by the design of physical devices and user interfaces. We advocate an integrated, end-to-end approach-combining algorithmic, engineering, and design perspectives-to enable more intuitive and effective interactions with intelligent systems.

Problem

• Balance user agency and system automation
• Enhance human-AI interaction efficiency
• Develop adaptive control methods for intelligent systems

Innovation

• Adjustable force spherical electromagnet system
• Integrated 3D tracking with haptic feedback
• Model-free reinforcement learning adaptive interfaces