Verification of RoboChart Models with Neural Network Components

Current software engineering frameworks for robotics treat artificial neural networks (ANNs) components as black boxes, and existing white-box techniques consider either component-level properties, or properties involving a specific case study. A method to establish properties that may depend on all components in such a system is, as yet, undefined. Our work consists of defining such a method. First, we developed a component whose behaviour is defined by an ANN and acts as a robotic controller. Considering our application to robotics, we focus on pre-trained ANNs used for control. We define our component in the context of RoboChart, where we define modelling notation involving a meta-model and well-formedness conditions, and a process-algebraic semantics. To further support our framework, we defined an implementation of these semantics in Java and CSPM, to enable validation and discretised verification. Given these components, we then developed an approach to verify software systems involving our ANN components. This approach involves replacing existing memoryless, cyclic, controller components with ANN components, and proving that the new system does not deviate in behaviour by more than a constant ε from the original system. Moreover, we describe a strategy for automating these proofs based on Isabelle and Marabou, combining ANN-specific verification tools with general verification tools. We demonstrate our framework using a case study involving a Segway robot where we replace a PID controller with an ANN component. Our contributions can be summarised as follows: we have generated a framework that enables the modelling, validation, and verification of robotic software involving neural network components. Finally, this work represents progress towards establishing the safety and reliability of autonomous robotics

Computer Aided Verification

This open access two-volume set LNCS 10980 and 10981 constitutes the refereed proceedings of the 30th International Conference on Computer Aided Verification, CAV 2018, held in Oxford, UK, in July 2018. The 52 full and 13 tool papers presented together with 3 invited papers and 2 tutorials were carefully reviewed and selected from 215 submissions. The papers cover a wide range of topics and techniques, from algorithmic and logical foundations of verification to practical applications in distributed, networked, cyber-physical, and autonomous systems. They are organized in topical sections on model checking, program analysis using polyhedra, synthesis, learning, runtime verification, hybrid and timed systems, tools, probabilistic systems, static analysis, theory and security, SAT, SMT and decisions procedures, concurrency, and CPS, hardware, industrial applications

Tools and Algorithms for the Construction and Analysis of Systems

This open access two-volume set constitutes the proceedings of the 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2021, which was held during March 27 – April 1, 2021, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2021. The conference was planned to take place in Luxembourg and changed to an online format due to the COVID-19 pandemic. The total of 41 full papers presented in the proceedings was carefully reviewed and selected from 141 submissions. The volume also contains 7 tool papers; 6 Tool Demo papers, 9 SV-Comp Competition Papers. The papers are organized in topical sections as follows: Part I: Game Theory; SMT Verification; Probabilities; Timed Systems; Neural Networks; Analysis of Network Communication. Part II: Verification Techniques (not SMT); Case Studies; Proof Generation/Validation; Tool Papers; Tool Demo Papers; SV-Comp Tool Competition Papers