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Found 17 papers, 1 papers with code
IMPaCT: Interval MDP Parallel Construction for Controller Synthesis of Large-Scale Stochastic Systems
This paper is concerned with developing a software tool, called IMPaCT, for the parallelized verification and controller synthesis of large-scale stochastic systems using interval Markov chains (IMCs) and interval Markov decision processes (IMDPs), respectively.
MDP Abstractions from Data: Large-Scale Stochastic Networks
This work proposes a compositional data-driven technique for the construction of finite Markov decision processes (MDPs) for large-scale stochastic networks with unknown mathematical models.
Symbolic Abstractions with Guarantees: A Data-Driven Divide-and-Conquer Strategy
We construct a symbolic abstraction from data for each room as an appropriate substitute of original system and compositionally synthesize controllers regulating the temperature of each room within a safe zone with some guaranteed probabilistic confidence.
Safety Barrier Certificates for Stochastic Control Systems with Wireless Communication Networks
This work is concerned with a formal approach for safety controller synthesis of stochastic control systems with both process and measurement noises while considering wireless communication networks between sensors, controllers, and actuators.
Compositional Controller Synthesis for Interconnected Stochastic Systems with Markovian Switching
We apply our results to a room temperature network of 200 rooms with Markovian switching signals while accepting multiple storage certificates.
Compositional Reinforcement Learning for Discrete-Time Stochastic Control Systems
A key contribution is to leverage the convergence results for adversarial RL (minimax Q-learning) on finite stochastic arenas to provide control strategies maximizing the probability of satisfaction over the network of continuous-space systems.
Safety Barrier Certificates for Stochastic Hybrid Systems
In our proposed scheme, we first provide an augmented framework to characterize each stochastic hybrid system containing continuous evolutions and instantaneous jumps with a unified system covering both scenarios.
Constructing MDP Abstractions Using Data with Formal Guarantees
We then propose a scenario convex program (SCP) associated to the original RCP by collecting a finite number of data from trajectories of the system.
Data-Driven Synthesis of Symbolic Abstractions with Guaranteed Confidence
In this work, we propose a data-driven approach for the construction of finite abstractions (a. k. a., symbolic models) for discrete-time deterministic control systems with unknown dynamics.
Data-driven Safety Verification of Stochastic Systems via Barrier Certificates
In this paper, we propose a data-driven approach to formally verify the safety of (potentially) unknown discrete-time continuous-space stochastic systems.
Formal Estimation of Collision Risks for Autonomous Vehicles: A Compositional Data-Driven Approach
The proposed approach is based on the construction of sub-barrier certificates for each stochastic agent via a set of data collected from its trajectories while providing an a-priori guaranteed confidence on the data-driven estimation.
Data-driven verification and synthesis of stochastic systems via barrier certificates
In this work, we study verification and synthesis problems for safety specifications over unknown discrete-time stochastic systems.
Automata-based Controller Synthesis for Stochastic Systems: A Game Framework via Approximate Probabilistic Relations
In this work, we propose an abstraction and refinement methodology for the controller synthesis of discrete-time stochastic systems to enforce complex logical properties expressed by deterministic finite automata (a. k. a.
Compositional Synthesis of Control Barrier Certificates for Networks of Stochastic Systems against $ω$-Regular Specifications
This paper is concerned with a compositional scheme for the construction of control barrier certificates for interconnected discrete-time stochastic systems.
Safe-visor Architecture for Sandboxing (AI-based) Unverified Controllers in Stochastic Cyber-Physical Systems
To cope with this difficulty, we propose in this work a Safe-visor architecture for sandboxing unverified controllers in CPSs operating in noisy environments (a. k. a.
From Small-Gain Theory to Compositional Construction of Barrier Certificates for Large-Scale Stochastic Systems
This paper is concerned with a compositional approach for the construction of control barrier certificates for large-scale interconnected stochastic systems while synthesizing hybrid controllers against high-level logic properties.
Formal Controller Synthesis for Continuous-Space MDPs via Model-Free Reinforcement Learning
A key contribution of the paper is to leverage the classical convergence results for reinforcement learning on finite MDPs and provide control strategies maximizing the probability of satisfaction over unknown, continuous-space MDPs while providing probabilistic closeness guarantees.
