9 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…
C-IDS: Solving Contextual POMDP via Information-Directed Objective
Chongyang Shi, Michael Dorothy, Jie Fu
We study the policy synthesis problem in contextual partially observable Markov decision processes (CPOMDPs), where the environment is governed by an unknown latent context that in…
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…
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…
Information-Theoretic Opacity-Enforcement in Markov Decision Processes
Chongyang Shi, Yuheng Bu, Jie Fu
The paper studies information-theoretic opacity, an information-flow privacy property, in a setting involving two agents: A planning agent who controls a stochastic system and an o…