Agents and Robots for Reliable Engineered Autonomy

This book contains the contributions of the Special Issue entitled "Agents and Robots for Reliable Engineered Autonomy". The Special Issue was based on the successful first edition of the "Workshop on Agents and Robots for reliable Engineered Autonomy" (AREA 2020), co-located with the 24th European Conference on Artificial Intelligence (ECAI 2020). The aim was to bring together researchers from autonomous agents, as well as software engineering and robotics communities, as combining knowledge from these three research areas may lead to innovative approaches that solve complex problems related to the verification and validation of autonomous robotic systems

Explanation of the Model Checker Verification Results

Immer wenn neue Anforderungen an ein System gestellt werden, müssen die Korrektheit und Konsistenz der Systemspezifikation überprüft werden, was in der Praxis in der Regel manuell erfolgt. Eine mögliche Option, um die Nachteile dieser manuellen Analyse zu überwinden, ist das sogenannte Contract-Based Design. Dieser Entwurfsansatz kann den Verifikationsprozess zur Überprüfung, ob die Anforderungen auf oberster Ebene konsistent verfeinert wurden, automatisieren. Die Verifikation kann somit iterativ durchgeführt werden, um die Korrektheit und Konsistenz des Systems angesichts jeglicher Änderung der Spezifikationen sicherzustellen. Allerdings ist es aufgrund der mangelnden Benutzerfreundlichkeit und der Schwierigkeiten bei der Interpretation von Verifizierungsergebnissen immer noch eine Herausforderung, formale Ansätze in der Industrie einzusetzen. Stellt beispielsweise der Model Checker bei der Verifikation eine Inkonsistenz fest, generiert er ein Gegenbeispiel (Counterexample) und weist gleichzeitig darauf hin, dass die gegebenen Eingabespezifikationen inkonsistent sind. Hier besteht die gewaltige Herausforderung darin, das generierte Gegenbeispiel zu verstehen, das oft sehr lang, kryptisch und komplex ist. Darüber hinaus liegt es in der Verantwortung der Ingenieurin bzw. des Ingenieurs, die inkonsistente Spezifikation in einer potenziell großen Menge von Spezifikationen zu identifizieren. Diese Arbeit schlägt einen Ansatz zur Erklärung von Gegenbeispielen (Counterexample Explanation Approach) vor, der die Verwendung von formalen Methoden vereinfacht und fördert, indem benutzerfreundliche Erklärungen der Verifikationsergebnisse der Ingenieurin bzw. dem Ingenieur präsentiert werden. Der Ansatz zur Erklärung von Gegenbeispielen wird mittels zweier Methoden evaluiert: (1) Evaluation anhand verschiedener Anwendungsbeispiele und (2) eine Benutzerstudie in Form eines One-Group Pretest-Posttest Experiments.Whenever new requirements are introduced for a system, the correctness and consistency of the system specification must be verified, which is often done manually in industrial settings. One viable option to traverse disadvantages of this manual analysis is to employ the contract-based design, which can automate the verification process to determine whether the refinements of top-level requirements are consistent. Thus, verification can be performed iteratively to ensure the system’s correctness and consistency in the face of any change in specifications. Having said that, it is still challenging to deploy formal approaches in industries due to their lack of usability and their difficulties in interpreting verification results. For instance, if the model checker identifies inconsistency during the verification, it generates a counterexample while also indicating that the given input specifications are inconsistent. Here, the formidable challenge is to comprehend the generated counterexample, which is often lengthy, cryptic, and complex. Furthermore, it is the engineer’s responsibility to identify the inconsistent specification among a potentially huge set of specifications. This PhD thesis proposes a counterexample explanation approach for formal methods that simplifies and encourages their use by presenting user-friendly explanations of the verification results. The proposed counterexample explanation approach identifies and explains relevant information from the verification result in what seems like a natural language statement. The counterexample explanation approach extracts relevant information by identifying inconsistent specifications from among the set of specifications, as well as erroneous states and variables from the counterexample. The counterexample explanation approach is evaluated using two methods: (1) evaluation with different application examples, and (2) a user-study known as one-group pretest and posttest experiment

Recent Trends in Computational Intelligence

Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications