Search Results for author:
Found 25 papers, 1 papers with code
Temporal Logic Resilience for Dynamical Systems
We use finite temporal logic to express the requirements on the acceptable functionality and define the resilience metric as the maximum disturbance under which the system satisfies the temporal requirements.
Safe Reach Set Computation via Neural Barrier Certificates
We present a novel technique for online safety verification of autonomous systems, which performs reachability analysis efficiently for both bounded and unbounded horizons by employing neural barrier certificates.
Data-Driven Distributionally Robust Safety Verification Using Barrier Certificates and Conditional Mean Embeddings
Algorithmic verification of realistic systems to satisfy safety and other temporal requirements has suffered from poor scalability of the employed formal approaches.
Formal Verification of Unknown Stochastic Systems via Non-parametric Estimation
A novel data-driven method for formal verification is proposed to study complex systems operating in safety-critical domains.
Assume-Guarantee Reinforcement Learning
We present a modular approach to \emph{reinforcement learning} (RL) in environments consisting of simpler components evolving in parallel.
Using Knowledge Awareness to improve Safety of Autonomous Driving
We present a method, which incorporates knowledge awareness into the symbolic computation of discrete controllers for reactive cyber physical systems, to improve decision making about the unknown operating environment under uncertain/incomplete inputs.
Generalized Stochastic Dynamic Aggregative Game for Demand-Side Management in Microgrids with Shared Battery
In this paper, we focus on modeling and analysis of demand-side management in a microgrid where agents utilize grid energy and a shared battery charged by renewable energy sources.
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.
Verifying the Unknown: Correct-by-Design Control Synthesis for Networks of Stochastic Uncertain Systems
We address two limitations of existing approaches for formal synthesis of controllers for networks of uncertain systems satisfying complex temporal specifications.
Neural Abstraction-Based Controller Synthesis and Deployment
For the selected benchmarks, our approach reduces the memory requirements respectively for the synthesis and deployment by a factor of $1. 31\times 10^5$ and $7. 13\times 10^3$ on average, and up to $7. 54\times 10^5$ and $3. 18\times 10^4$.
Bayesian Formal Synthesis of Unknown Systems via Robust Simulation Relations
With a focus on continuous-space stochastic systems with parametric uncertainty, we propose a two-stage approach that decomposes the problem into a learning stage and a robust formal controller synthesis stage.
SySCoRe: Synthesis via Stochastic Coupling Relations
We present SySCoRe, a MATLAB toolbox that synthesizes controllers for stochastic continuous-state systems to satisfy temporal logic specifications.
Correct-by-Design Control of Parametric Stochastic Systems
We develop new methods for models of systems subject to both stochastic and parametric uncertainties.
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 Abstraction-Based Control Synthesis
The growth bound together with the sampled trajectories are then used to construct the abstraction and synthesise a controller.
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.
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.
Symbolic Control for Stochastic Systems via Finite Parity Games
While characterizing the exact satisfaction probability is open, we show that a lower bound on this probability can be obtained by (I) computing an under-approximation of the qualitative winning region, i. e., states from which the parity condition can be enforced almost surely, and (II) computing the maximal probability of reaching this qualitative winning region.
Compositional Construction of Control Barrier Functions for Continuous-Time Stochastic Hybrid Systems
In this work, we propose a compositional framework for the construction of control barrier functions for networks of continuous-time stochastic hybrid systems enforcing complex logic specifications expressed by finite-state automata.
Data-Driven Verification under Signal Temporal Logic Constraints
Since the dynamics are parameterized and partially unknown, we collect data from the system and employ Bayesian inference techniques to associate a confidence value to the satisfaction of the property.
Formal Policy Synthesis for Continuous-Space Systems via Reinforcement Learning
We use this procedure to guide the RL algorithm towards a policy that converges to an optimal policy under suitable assumptions on the process.
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.
Perception-in-the-Loop Adversarial Examples
We run CMA-ES using human participants to provide the fitness function, using the insight that the choice of best candidates in CMA-ES can be naturally modeled as a perception task: pick the top $k$ inputs perceptually closest to a fixed input.
