Leveraging autonomous systems in safety-critical scenarios requires verifying their behaviors in the presence of uncertainties and black-box components that influence the system dynamics. In this work, we develop a framework for verifying discrete-time dynamical systems with unmodelled dynamics and noisy measurements against temporal logic specifications from an input-output dataset. The verification framework employs Gaussian process (GP) regression to learn the unknown dynamics from the dataset and abstracts the continuous-space system as a finite-state, uncertain Markov decision process (MDP). This abstraction relies on space discretization and transition probability intervals that capture the uncertainty due to the error in GP regression by using reproducible kernel Hilbert space analysis as well as the uncertainty induced by discretization. The framework utilizes existing model checking tools for verification of the uncertain MDP abstraction against a given temporal logic specification. We establish the correctness of extending the verification results on the abstraction created from noisy measurements to the underlying system. We show that the computational complexity of the framework is polynomial in the size of the dataset and discrete abstraction. The complexity analysis illustrates a trade-off between the quality of the verification results and the computational burden to handle larger datasets and finer abstractions. Finally, we demonstrate the efficacy of our learning and verification framework on several case studies with linear, nonlinear, and switched dynamical systems.
GDPR Auto-Formalization with AI Agents and Human Verification
Center for Juris-Informatics, ROIS-DS（法律信息中心，ROIS-DS）；Ruhr-University Bochum, RC-Trust（波恩鲁尔大学，RC-Trust）；Alberta Machine Intelligence Institute, University of Alberta（阿尔伯塔机器智能研究所，阿尔伯塔大学）
Agentic AI-based Coverage Closure for Formal Verification
1 Infineon Technologies India Private Limited, India 2 Dr. B R Ambedkar National Institute of Technology Jalandhar, India 3 Infineon Technologies Dresden AG \& Co. KG, Germany
The coordination of multiple autonomous vehicles into convoys or platoons is expected on our highways in the near future. However, before such platoons can be deployed, the new autonomous behaviors of the vehicles in these platoons must be certified. An appropriate representation for vehicle platooning is as a multi-agent system in which each agent captures the "autonomous decisions" carried out by each vehicle. In order to ensure that these autonomous decision-making agents in vehicle platoons never violate safety requirements, we use formal verification. However, as the formal verification t…
Groundbreaking successes have been achieved by Deep Reinforcement Learning (DRL) in solving practical decision-making problems. Robotics, in particular, can involve high-cost hardware and human interactions. Hence, scrupulous evaluations of trained models are required to avoid unsafe behaviours in the operational environment. However, designing metrics to measure the safety of a neural network is an open problem, since standard evaluation parameters (e.g., total reward) are not informative enough. In this paper, we present a semi-formal verification approach for decision-making tasks, based on…
The deployment of autonomous systems that operate in unstructured environments necessitates algorithms to verify their safety. This can be challenging due to, e.g., black-box components in the control software, or undermodelled dynamics that prevent model-based verification. We present a novel verification framework for an unknown dynamical system from a given set of noisy observations of the dynamics. Using Gaussian processes trained on this data set, the framework abstracts the system as an uncertain Markov process with discrete states defined over the safe set. The transition bounds of the …
In this paper, we propose a data-driven approach to formally verify the safety of (potentially) unknown discrete-time continuous-space stochastic systems. The proposed framework is based on a notion of barrier certificates together with data collected from trajectories of unknown systems. We first reformulate the barrier-based safety verification as a robust convex problem (RCP). Solving the acquired RCP is hard in general because not only the state of the system lives in a continuous set, but also and more problematic, the unknown model appears in one of the constraints of RCP. Instead, we le…
The emergence of Context-aware systems in the domains of autonomic, monitoring, and safety-critical applications asks for the definition of methods to formally assess their correctness and dependability properties. Many of these properties are common to Automatic Control systems, a field that developed well established analysis and design techniques to formalize and investigate them. In this paper, we use Boolean Control Networks, to discuss some properties of a feedback Context-aware system in a case study based on a healthcare management example.
Recent employments of SMT solvers within the Lyapunov function synthesis provided effective tools for automated construction of Lyapunov functions alongside with sound computer-assisted certificates. The main benefit of the suggested approach is the formal correctness and elimination of the numerical uncertainty. In the present work, we extend the SMT-based synthesis approach for wider classes of continuous and discrete-time systems. Additionally, we address constructions of Lyapunov functions for state-dependent switching systems. We illustrate our approach by means of various examples from t…
Heriot-Watt University（赫瑞-瓦特大学）；University of Liverpool（利物浦大学）
The battery is a key component of autonomous robots. Its performance limits the robot's safety and reliability. Unlike liquid-fuel, a battery, as a chemical device, exhibits complicated features, including (i) capacity fade over successive recharges and (ii) increasing discharge rate as the state of charge (SOC) goes down for a given power demand. Existing formal verification studies of autonomous robots, when considering energy constraints, formalise the energy component in a generic manner such that the battery features are overlooked. In this paper, we model an unmanned aerial vehicle (UAV)…
This paper introduces the concept of plant model generation from the recorded traces of events using the process mining technique. The event logs are obtained by visually simulating a simple distributed manufacturing system using the OPC UA communication protocol. The process discovery alpha algorithm is used to extract the process model in Petri net format. The system behavior represented in terms of Petri net is then fed to construct the ECC of the basic function block in compliance with IEC 61499 standard using the proposed notation. Finally, the formal verification of the closed-loop syste…
Low-code development platforms are gaining popularity. Essentially, such platforms allow to shift from coding to graphical modeling, helping to improve quality and reduce development time. The Cordis SUITE is a low-code development platform that adopts the Unified Modeling Language (UML) to design complex machine-control applications. In this paper we introduce Cordis models and their semantics. To enable formal verification, we define an automatic translation of Cordis models to the process algebraic specification language mCRL2. As a proof of concept, we describe requirements of the control …
For the implementations of controllers on digital processors, certain limitations, e.g. in the instruction set and register length, need to be taken into account, especially for safety-critical applications. This work aims to provide a computer-certified inductive definition for the control functions that are implemented on such processors accompanied with the fixed-point data type in a proof assistant. Using these inductive definitions we formally ensure correct realization of the controllers on a digital processor. Our results guarantee overflow-free computations of the implemented control a…
