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Reo is a visual language of connectors that originated in component-based software engineering. It is a flexible and intuitive language, yet powerful and capable of expressing complex patterns of composition. The intricacies of the language resulted in many semantic models proposed for Reo, including several automata-based ones.

Synchronous Kleene algebra (SKA), an extension of Kleene algebra (KA), was proposed by Prisacariu as a tool for reasoning about programs that may execute synchronously, i.e., in lock-step. We provide a countermodel witnessing that the axioms of SKA are incomplete w.r.t. its language semantics, by exploiting a lack of interaction between the synchronous product operator and the Kleene star. We then propose an alternative set of axioms for SKA, based on Salomaa's axiomatisation of regular languages, and show that these provide a sound and complete characterisation w.r.t. the original language semantics., Comment: Accepted at MPC 2019

Domain Specific Languages (DSLs) provide a lightweight approach to incorporate formal techniques into the industrial workflow. From DSL instances, formal models and other artefacts can be generated, such as simulation models and code. Having a single source for all artefacts improves maintenance and offers a high return on investment of the initial modelling effort. Since DSLs can be used to capture essential domain information at a high level of abstraction, this supports formal verification early in the development process. We discuss our experiences with this approach in a number of real industrial development projects. cop. Springer International Publishing Switzerland 2016.

Contains fulltext : 159576.pdf (Author’s version preprint ) (Open Access)

In this paper we concern ourselves with normative multi-agent systems, which are multi-agent systems governed by a set of norms. In these systems, the internals and architecture of the participating agents may be unknown to us, which disables us to make any strong assumption on the possible behaviour that these agents may exhibit. Thus, we cannot simply assume that the agents are aware of the norms, or that they are compliant with respect to the norms. In other words, a crucial problem that needs to be solved is how we can verify these systems if we have no idea whether the agents will be norm-obedient. This paper investigates two distinct formal frameworks which allow us to tackle this problem, namely in the first part of this paper we propose a logic-based framework which uses compliance types, and in the second part we propose a framework which tackles the problem from a mechanism-design perspective.

We study weighted automata from both an algebraic and a coalgebraic perspective. In particular, we consider equations and coequations for weighted automata. We prove a duality result that relates sets of equations (congruences) with (certain) subsets of coequations. As a consequence, we obtain two equivalent but complementary ways to define classes of weighted automata. We show that this duality cannot be generalized to linear congruences in general but we obtain partial results when weights are from a field.

Modeling and analysis of cyber-physical systems are still challenging. One reason is that cyber-physical systems involve many different parts (cyber or physical), of different nature (discrete or continuous), and in constant interaction via sensing and actuating.For instance, consider a group of robots, each running a program that takes decision based on the sequence of sensor readings. The sensors that equip a robot return the current position of the robot and the position of any adjacent obstacle. The interactionoccurring between each robot in the group cannot be derived solely from the specification of individual robots. If the field on which the robots roam changes its property, the same group of robots might sense different values, and therefore take different actions. Also, the time at which a robot acts and senses will affect the decision of each controller and will change the resulting collective behavior.This thesis proposes a compositional approach to the design and programming of interacting cyber-physical components. We present an algebra that provides a novel perspective on modeling interaction of cyber-physical components. Using our algebraic framework, one can design a complex cyber-physical system by first designing each part, and then specifying how the parts interact. We formalized the relation between our abstract semantic model and an implementation written in Maude, a programming language based on rewriting logic. We present some applications, including safety and liveness properties of a system consisting of a set of robots, each equipped with a battery, running on a shared field.

The research topic of the thesis is the extension of evolutionary multi-objective optimization for real-world scheduling problems. Several novel algorithms are proposed: the diversity indicator-based multi-objective evolutionary algorithm (DI-MOEA) can achieve a uniformly distributed solution set; the preference-based MOEA can obtain preferred solutions; the edge-rotated cone can improve the performance of MOEAs for many-objective optimization; and dynamic MOEA takes the stability as an extra objective. Besides the classical flexible job shop scheduling, the thesis proposes solutions for the novel problem domain of vehicle fleet maintenance scheduling optimization (VFMSO). The problem originated from the CIMPLO (Cross-Industry Predictive Maintenance Optimization Platform) project and the project partners Honda and KLM. The VFMSO problem is to determine the maintenance schedule for the vehicle fleet, meaning to find the best maintenance order, location and time for each component in the vehicle fleet based on the predicted remaining useful lifetimes of components and conditions of available workshops. The maintenance schedule is optimized to bring business advantages to industries, i.e., to reduce maintenance time, increase safety and save repair expenses. After formulating the problem as a scalable benchmark in an industrially relevant setting, the proposed algorithms have been successfully used to solve VFMSO problem instances.

In deep reinforcement learning, searching and learning techniques are two important components. They can be used independently and in combination to deal with different problems in AI. These results have inspired research into artificial general intelligence (AGI).We study table based classic Q-learning on the General Game Playing (GGP) system, showing that classic Q-learning works on GGP, although convergence is slow, and it is computationally expensive to learn complex games.This dissertation uses an AlphaZero-like self-play framework to explore AGI on small games. By tuning different hyper-parameters, the role, effects and contributions of searching and learning are studied. A further experiment shows that search techniques can contribute as experts to generate better training examples to speed up the start phase of training.In order to extend the AlphaZero-likeself-play approach to single player complex games, the Morpion Solitaire game is implemented by combining Ranked Reward method. Our first AlphaZero-based approach is able to achieve a near human best record.Overall, in this thesis, both searching and learning techniques are studied (by themselves and in combination) in GGP and AlphaZero-like self-play systems. We do so for the purpose of making steps towards artificial general intelligence, towards systems that exhibit intelligent behavior in more than one domain.

In deep reinforcement learning, searching and learning techniques are two important components. They can be used independently and in combination to deal with different problems in AI. These results have inspired research into artificial general intelligence (AGI).We study table based classic Q-learning on the General Game Playing (GGP) system, showing that classic Q-learning works on GGP, although convergence is slow, and it is computationally expensive to learn complex games.This dissertation uses an AlphaZero-like self-play framework to explore AGI on small games. By tuning different hyper-parameters, the role, effects and contributions of searching and learning are studied. A further experiment shows that search techniques can contribute as experts to generate better training examples to speed up the start phase of training.In order to extend the AlphaZero-likeself-play approach to single player complex games, the Morpion Solitaire game is implemented by combining Ranked Reward method. Our first AlphaZero-based approach is able to achieve a near human best record.Overall, in this thesis, both searching and learning techniques are studied (by themselves and in combination) in GGP and AlphaZero-like self-play systems. We do so for the purpose of making steps towards artificial general intelligence, towards systems that exhibit intelligent behavior in more than one domain.

Machine Learning is becoming a more and more substantial technology for industry. It allows to learn from previous experiences in an automated way to make decisions based on the learned behavior. Machine learning enables the development of completely new products like autonomous driving or services which are purely driven by data.The development of such new data-driven products is often a long procedure. Even the application of machine learning algorithms to specific problems is mostly not straightforward. To illustrate this, a data-driven service, called Automated Damage Assessment, from the automotive industry is introduced in this work. Based on the gained experience from such data-driven service developments, this dissertation proposes a methodology to develop data-driven services in an accurate and fast manner. The Automated Damage Assessment service is based on sensor data, i.e. data recorded from vehicle on-board sensors over time. Using such time series from more than one sensor results in a multivariate time series. The existent methods to solve multivariate time series classification-problems are often complex and developed for specific problems without being scalable. To overcome this, suitable approaches with different complexities are proposed in this work. These approaches are applied on multiple publicly available data sets and on real-world data sets from medical and industrial domain with the result that especially two AutoML (Automated Machine Learning) approaches, namely GAMA and ATM, as well as one of the proposed approaches (PHCP) are most suitable to solve these particular multivariate time series problems.

This thesis focuses on addressing four research problems in designing embedded streaming systems. Embedded streaming systems are those systems thatprocess a stream of input data coming from the environment and generate a stream of output data going into the environment. For many embeddedstreaming systems, the timing is a critical design requirement, in which the correct behavior depends on both the correctness of output data and on the time at which the data is produced. An embedded streaming system subjected to such a timing requirement is called a real-time system. Some examples of real-time embedded streaming systems can be found in various autonomous mobile systems, such as planes, self-driving cars, and drones. To handle the tight timing requirements of such real-time embedded streaming systems, modern embedded systems have been equipped with hardware platforms, the so-called Multi-Processor Systems-on-Chip (MPSoC), that contain multiple processors, memories, interconnections, and other hardware peripherals on a single chip, to benefit from parallel execution. To efficiently exploit the computational capacity of an MPSoC platform, a streaming application which is going to be executed on the MPSoC platform must be expressed primarily in a parallel fashion, i.e., the application is represented as a set of parallel executing and communicating tasks. Then, the main challenge is how to schedule the tasks spatially, i.e., task mapping, and temporally, i.e., task scheduling, on the MPSoC platform such that all timing requirements are satisfied while making efficient utilization of available resources (e.g, processors, memory, energy, etc.) on the platform. Another challenge is how to implement and run the mapped and scheduled application tasks on the MPSoC platform. This thesis proposes several techniques to address the aforementioned two challenges.

The Matlab/Simulink language has become the standard formalism for modeling and implementing control software in areas like avionics, automotive, railway, and process automation. Such software is often safety critical, and bugs have potentially disastrous consequences for people and material involved. We define a verification methodology to assess the correctness of Simulink programs by means of automated test-case generation. In the style of fault- and mutation-based testing, the coverage of a Simulink program by a test suite is defined in terms of the detection of injected faults. Using bounded model checking techniques, we are able to effectively and automatically compute test suites for given fault models. Several optimisations are discussed to make the approach practical for realistic Simulink programs and fault models, and to obtain accurate coverage measures.

The main goal of this dissertation is to manage resource allocation in network engineering problems and to introduce efficient cooperative algorithms to obtain high performance, ensuring fairness and stability. Specifically, this dissertation introduces new approaches for resource allocation in Orthogonal Frequency Division Multiple Access (OFDMA) wireless networks and in smart power grids by casting the problems to the coalitional game framework and by providing a constructive iterative algorithm based on dynamic learning theory.