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Human Behavior Identification for Linear Systems in Adversarial Environments by Adaptive Inverse Reinforcement
IEEE Transactions on Cybernetics
|November 19, 2025
Summary
This study introduces a new method for identifying human behavior in human-in-the-loop (HiTL) systems facing adversarial conditions. The approach uses adaptive inverse reinforcement learning (IRL) to understand human decision-making without needing control input data.
Area of Science:
- Control Systems Engineering
- Artificial Intelligence
- Human-Computer Interaction
Background:
- Human-in-the-loop (HiTL) systems are increasingly complex, especially in adversarial environments.
- Identifying human behavior is crucial for predicting system performance and ensuring safety.
- Existing methods for human behavior identification often have limitations, such as requiring persistent excitation or direct measurement of control inputs.
Purpose of the Study:
- To develop a novel method for human behavior identification in linear HiTL systems operating in adversarial settings.
- To overcome limitations of existing approaches by removing the need for persistent excitation and control input measurement.
- To model the human and adversarial environment as players in a zero-sum differential game.
Main Methods:
- Formulated the HiTL system as a linear-quadratic zero-sum differential game.
- Transformed human behavior identification into an inverse reinforcement learning (IRL) problem.
- Proposed an integral concurrent learning (ICL) law to estimate the human's feedback matrix.
- Retrieved human cost function weighting matrices by minimizing a residual based on the estimated feedback matrix.
Main Results:
- Successfully estimated the human feedback matrix using the proposed ICL law.
- Accurately retrieved human cost function weighting matrices.
- Demonstrated the method's validity through simulations and experiments in a vehicle lane-keeping scenario.
- Validated the adaptive-IRL-based human behavior identification strategy.
Conclusions:
- The proposed adaptive-IRL-based strategy effectively identifies human behavior in adversarial HiTL systems.
- The method removes the need for persistent excitation and control input measurement, offering a significant advantage over existing techniques.
- This research contributes to more robust and predictable human-AI interaction in safety-critical applications.
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