When to Trust AI: Advances and Challenges for Certification of Neural Networks

Artificial intelligence (AI) has been advancing at a fast pace and it is now poised for deployment in a wide range of applications, such as autonomous systems, medical diagnosis and natural language processing. Early adoption of AI technology for real-world applications has not been without problems, particularly for neural networks, which may be unstable and susceptible to adversarial examples. In the longer term, appropriate safety assurance techniques need to be developed to reduce potential harm due to avoidable system failures and ensure trustworthiness. Focusing on certification and explainability, this paper provides an overview of techniques that have been developed to ensure safety of AI decisions and discusses future challenges

SAT-based Analysis, (Re-)Configuration & Optimization in the Context of Automotive Product documentation

Es gibt einen steigenden Trend hin zu kundenindividueller Massenproduktion (mass customization), insbesondere im Bereich der Automobilkonfiguration. Kundenindividuelle Massenproduktion führt zu einem enormen Anstieg der Komplexität. Es gibt Hunderte von Ausstattungsoptionen aus denen ein Kunde wählen kann um sich sein persönliches Auto zusammenzustellen. Die Anzahl der unterschiedlichen konfigurierbaren Autos eines deutschen Premium-Herstellers liegt für ein Fahrzeugmodell bei bis zu 10^80. SAT-basierte Methoden haben sich zur Verifikation der Stückliste (bill of materials) von Automobilkonfigurationen etabliert. Carsten Sinz hat Mitte der 90er im Bereich der SAT-basierten Verifikationsmethoden für die Daimler AG Pionierarbeit geleistet. Darauf aufbauend wurde nach 2005 ein produktives Software System bei der Daimler AG installiert. Später folgten weitere deutsche Automobilhersteller und installierten ebenfalls SAT-basierte Systeme zur Verifikation ihrer Stücklisten. Die vorliegende Arbeit besteht aus zwei Hauptteilen. Der erste Teil beschäftigt sich mit der Entwicklung weiterer SAT-basierter Methoden für Automobilkonfigurationen. Wir zeigen, dass sich SAT-basierte Methoden für interaktive Automobilkonfiguration eignen. Wir behandeln unterschiedliche Aspekte der interaktiven Konfiguration. Darunter Konsistenzprüfung, Generierung von Beispielen, Erklärungen und die Vermeidung von Fehlkonfigurationen. Außerdem entwickeln wir SAT-basierte Methoden zur Verifikation von dynamischen Zusammenbauten. Ein dynamischer Zusammenbau repräsentiert die chronologische Zusammenbau-Reihenfolge komplexer Teile. Der zweite Teil beschäftigt sich mit der Optimierung von Automobilkonfigurationen. Wir erläutern und vergleichen unterschiedliche Optimierungsprobleme der Aussagenlogik sowie deren algorithmische Lösungsansätze. Wir beschreiben Anwendungsfälle aus der Automobilkonfiguration und zeigen wie diese als aussagenlogisches Optimierungsproblem formalisiert werden können. Beispielsweise möchte man zu einer Menge an Ausstattungswünschen ein Test-Fahrzeug mit minimaler Ergänzung weiterer Ausstattungen berechnen um Kosten zu sparen. DesWeiteren beschäftigen wir uns mit der Problemstellung eine kleinste Menge an Fahrzeugen zu berechnen um eine Testmenge abzudecken. Im Rahmen dieser Arbeit haben wir einen Prototypen eines (Re-)Konfigurators, genannt AutoConfig, entwickelt. Unser (Re-)Konfigurator verwendet im Kern SAT-basierte Methoden und besitzt eine grafische Benutzeroberfläche, welche interaktive Konfiguration erlaubt. AutoConfig kann mit Instanzen von drei großen deutschen Automobilherstellern umgehen, aber ist nicht alleine darauf beschränkt. Mit Hilfe dieses Prototyps wollen wir die Anwendbarkeit unserer Methoden demonstrieren

A Maximum Satisfiability Based Approach to Bi-Objective Boolean Optimization

Many real-world problem settings give rise to NP-hard combinatorial optimization problems. This results in a need for non-trivial algorithmic approaches for finding optimal solutions to such problems. Many such approaches—ranging from probabilistic and meta-heuristic algorithms to declarative programming—have been presented for optimization problems with a single objective. Less work has been done on approaches for optimization problems with multiple objectives. We present BiOptSat, an exact declarative approach for finding so-called Pareto-optimal solutions to bi-objective optimization problems. A bi-objective optimization problem arises for example when learning interpretable classifiers and the size, as well as the classification error of the classifier should be taken into account as objectives. Using propositional logic as a declarative programming language, we seek to extend the progress and success in maximum satisfiability (MaxSAT) solving to two objectives. BiOptSat can be viewed as an instantiation of the lexicographic method and makes use of a single SAT solver that is preserved throughout the entire search procedure. It allows for solving three tasks for bi-objective optimization: finding a single Pareto-optimal solution, finding one representative solution for each Pareto point, and enumerating all Pareto-optimal solutions. We provide an open-source implementation of five variants of BiOptSat, building on different algorithms proposed for MaxSAT. Additionally, we empirically evaluate these five variants, comparing their runtime performance to that of three key competing algorithmic approaches. The empirical comparison in the contexts of learning interpretable decision rules and bi-objective set covering shows practical benefits of our approach. Furthermore, for the best-performing variant of BiOptSat, we study the effects of proposed refinements to determine their effectiveness

Computing explanations for interactive constraint-based systems

Constraint programming has emerged as a successful paradigm for modelling combinatorial problems arising from practical situations. In many of those situations, we are not provided with an immutable set of constraints. Instead, a user will modify his requirements, in an interactive fashion, until he is satisfied with a solution. Examples of such applications include, amongst others, model-based diagnosis, expert systems, product configurators. The system he interacts with must be able to assist him by showing the consequences of his requirements. Explanations are the ideal tool for providing this assistance. However, existing notions of explanations fail to provide sufficient information. We define new forms of explanations that aim to be more informative. Even if explanation generation is a very hard task, in the applications we consider, we must manage to provide a satisfactory level of interactivity and, therefore, we cannot afford long computational times. We introduce the concept of representative sets of relaxations, a compact set of relaxations that shows the user at least one way to satisfy each of his requirements and at least one way to relax them, and present an algorithm that efficiently computes such sets. We introduce the concept of most soluble relaxations, maximising the number of products they allow. We present algorithms to compute such relaxations in times compatible with interactivity, achieving this by indifferently making use of different types of compiled representations. We propose to generalise the concept of prime implicates to constraint problems with the concept of domain consequences, and suggest to generate them as a compilation strategy. This sets a new approach in compilation, and allows to address explanation-related queries in an efficient way. We define ordered automata to compactly represent large sets of domain consequences, in an orthogonal way from existing compilation techniques that represent large sets of solutions

The 1991 Goddard Conference on Space Applications of Artificial Intelligence

The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The papers in this proceeding fall into the following areas: Planning and scheduling, fault monitoring/diagnosis/recovery, machine vision, robotics, system development, information management, knowledge acquisition and representation, distributed systems, tools, neural networks, and miscellaneous applications