6 papers
Safe-visor Architecture for Sandboxing (AI-based) Unverified Controllers in Stochastic Cyber-Physical Systems
Bingzhuo Zhong, Abolfazl Lavaei, Hongpeng Cao +2
High performance but unverified controllers, e.g., artificial intelligence-based (a.k.a. AI-based) controllers, are widely employed in cyber-physical systems (CPSs) to accomplish c…
AMYTISS: Parallelized Automated Controller Synthesis for Large-Scale Stochastic Systems
Abolfazl Lavaei, Mahmoud Khaled, Sadegh Soudjani +1
In this paper, we propose a software tool, called AMYTISS, implemented in C++/OpenCL, for designing correct-by-construction controllers for large-scale discrete-time stochastic sys…
Formal Controller Synthesis for Continuous-Space MDPs via Model-Free Reinforcement Learning
Abolfazl Lavaei, Fabio Somenzi, Sadegh Soudjani +2
A novel reinforcement learning scheme to synthesize policies for continuous-space Markov decision processes (MDPs) is proposed. This scheme enables one to apply model-free, off-the…
Compositional Abstraction-based Synthesis for Networks of Stochastic Switched Systems
Abolfazl Lavaei, Sadegh Soudjani, Majid Zamani
In this paper, we provide a compositional approach for constructing finite abstractions (a.k.a. finite Markov decision processes (MDPs)) of interconnected discrete-time stochastic…
Compositional Abstraction-based Synthesis of General MDPs via Approximate Probabilistic Relations
Abolfazl Lavaei, Sadegh Soudjani, Majid Zamani
We propose a compositional approach for constructing abstractions of general Markov decision processes using approximate probabilistic relations. The abstraction framework is based…
Compositional Abstraction of Large-Scale Stochastic Systems: A Relaxed Dissipativity Approach
Abolfazl Lavaei, Sadegh Soudjani, Majid Zamani
In this paper, we propose a compositional approach for the construction of finite abstractions (a.k.a. finite Markov decision processes (MDPs)) for networks of discrete-time stocha…