Biblio

The proliferation of artificial intelligence based systems in all walks of life raises concerns about their safety and robustness, especially for cyber-physical systems including multiple machine learning components. In this paper, we introduce robustness contracts as a framework for compositional specification and reasoning about the robustness of cyber-physical systems based on neural network (NN) components. Robustness contracts can encompass and generalize a variety of notions of robustness which were previously proposed in the literature. They can seamlessly apply to NN-based perception as well as deep reinforcement learning (RL)-enabled control applications. We present a sound and complete algorithm that can efficiently verify the satisfaction of a class of robustness contracts on NNs by leveraging notions from Lagrangian duality to identify system configurations that violate the contracts. We illustrate the effectiveness of our approach on the verification of NN-based perception systems and deep RL-based control systems.

New generations of industrial control systems offer higher performance, they are distributed, and it is very likely that they are internet connected in one way or another. These trends raise new challenges in the contexts of reliability and security. We propose a novel approach that tackles the complexity of industrial control systems at design time and run time. At design time our target is to ease the configuration and verification of controller configurations through model-driven engineering techniques together with the contract-based design paradigm. At run time the information from design time is reused in order to support a modular and distributed self-adaptive software system that aims to increase reliability and security. The industrial setting of the presented approach are control devices for hydropower plant units.