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Stochastic control with distributionally robust constraints for cyber–physical systems vulnerable to attacks

https://doi.org/10.1016/j.ejcon.2024.101044

Venkatesh, Nishanth; Dave, Aditya; Faros, Ioannis; Malikopoulos, Andreas A (November 2024, European Journal of Control)

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Worst-Case Control and Learning Using Partial Observations Over an Infinite Time Horizon

https://doi.org/10.1109/CDC49753.2023.10383238

Dave, Aditya; Faros, Ioannis; Venkatesh, Nishanth; Malikopoulos, Andreas A (December 2023, IEEE)

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A Q-Learning Approach for Adherence-Aware Recommendations

https://doi.org/10.1109/LCSYS.2023.3339591

Faros, Ioannis; Dave, Aditya; Malikopoulos, Andreas A (January 2023, IEEE Control Systems Letters)

In many real-world scenarios involving high-stakes and safety implications, a human decision-maker (HDM) may receive recommendations from an artificial intelligence while holding the ultimate responsibility of making decisions. In this letter, we develop an “adherence-aware Q-learning” algorithm to address this problem. The algorithm learns the “adherence level” that captures the frequency with which an HDM follows the recommended actions and derives the best recommendation law in real time. We prove the convergence of the proposed Q-learning algorithm to the optimal value and evaluate its performance across various scenarios.
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