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eess.SY2025
Policy Gradient Methods for Information-Theoretic Opacity in Markov Decision Processes
Chongyang Shi, Sumukha Udupa, Michael R. Dorothy +2
Opacity, or non-interference, is a property ensuring that an external observer cannot infer confidential information (the "secret") from system observations. We introduce an inform…
eess.SY2025
Planning Stealthy Backdoor Attacks in MDPs with Observation-Based Triggers
Xinyi Wei, Shuo Han, Ahmed H. Hemida +2
This paper investigates backdoor attack planning in stochastic control systems modeled as Markov Decision Processes (MDPs). A backdoor attack involves an adversary deploying a poli…
eess.SY2025
Active Inference via Incentive Design: Eliciting Divergent Behaviors under Partial Observability
Xinyi Wei, Chongyang Shi, Shuo Han +3
We introduce Active Inference through Incentive Design, a framework in which a leader offers strategic incentive to elicit diverging behaviors from followers of different types, th…