5 papers · 1 filter
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…
IMAS: Joint Agent Selection and Information-Theoretic Coordinated Perception In Dec-POMDPs
Chongyang Shi, Wesley A. Suttle, Michael Dorothy +1
We study the problem of jointly selecting sensing agents and synthesizing decentralized active perception policies for the chosen subset of agents within a Decentralized Partially…
Integrated Control and Active Perception in POMDPs for Temporal Logic Tasks and Information Acquisition
Chongyang Shi, Michael R. Dorothy, Jie Fu
This paper studies the synthesis of a joint control and active perception policy for a stochastic system modeled as a partially observable Markov decision process (POMDP), subject…
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…
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…