5 papers · 1 filter
Incentive Design for Multi-Agent Systems: A Bilevel Optimization Framework for Coordinating Independent Agents and Convergence Analysis
Xinyi Wei, Shuo Han, Jie Fu
Incentive design aims to guide the performance of a system towards a human's intention or preference. We study this problem in a multi-agent system with one leader and multiple fol…
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…
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 through Incentive Design in Markov Decision Processes
Xinyi Wei, Chongyang Shi, Shuo Han +3
We present a method for active inference with partial observations in stochastic systems through incentive design, also known as the leader-follower game. Consider a leader agent w…
Active Perception with Initial-State Uncertainty: A Policy Gradient Method
Chongyang Shi, Shuo Han, Michael Dorothy +1
This paper studies the synthesis of an active perception policy that maximizes the information leakage of the initial state in a stochastic system modeled as a hidden Markov model…