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    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.

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    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.