12 citations · 37 across the 12 of their papers we have counts for
6 papers · 1 filter
Secure Planning Against Stealthy Attacks via Model-Free Reinforcement Learning
Alper Kamil Bozkurt, Yu Wang, Miroslav Pajic
We consider the problem of security-aware planning in an unknown stochastic environment, in the presence of attacks on control signals (i.e., actuators) of the robot. We model the…
Model-Free Reinforcement Learning for Stochastic Games with Linear Temporal Logic Objectives
Alper Kamil Bozkurt, Yu Wang, Michael Zavlanos +1
We study the problem of synthesizing control strategies for Linear Temporal Logic (LTL) objectives in unknown environments. We model this problem as a turn-based zero-sum stochasti…
Verifying Stochastic Hybrid Systems with Temporal Logic Specifications via Model Reduction
Yu Wang, Nima Roohi, Matthew West +2
We present a scalable methodology to verify stochastic hybrid systems. Using the Mori-Zwanzig reduction method, we construct a finite state Markov chain reduction of a given stocha…
Probabilistic Conformance for Cyber-Physical Systems
Yu Wang, Mojtaba Zarei, Borzoo Bonakdarpoor +1
In system analysis, conformance indicates that two systems simultaneously satisfy the same set of specifications of interest; thus, the results from analyzing one system automatica…
Context-Aware Temporal Logic for Probabilistic Systems
Mahmoud Elfar, Yu Wang, Miroslav Pajic
In this paper, we introduce the context-aware probabilistic temporal logic (CAPTL) that provides an intuitive way to formalize system requirements by a set of PCTL objectives with…
Statistically Model Checking PCTL Specifications on Markov Decision Processes via Reinforcement Learning
Yu Wang, Nima Roohi, Matthew West +2
Probabilistic Computation Tree Logic (PCTL) is frequently used to formally specify control objectives such as probabilistic reachability and safety. In this work, we focus on model…