Controller design for continuous dynamical systems is a core algorithmic problem in the design of cyber-physical systems (CPS). When the CPS application is safety critical, additionally we require the controller to have strong correctness guarantees. One approach for this design problem is to use simpler discrete abstraction of the original continuous system, on which known reactive synthesis methods can be used to design the controller. This approach is known as the abstraction-based controller design (ABCD) paradigm. In this thesis, we build ABCD procedures which are faster and more modular compared to the state-of-the-art, and can handle problems which were beyond the scope of the existing techniques. Usually, existing ABCD approaches use state space discretization for computing the abstractions, for which the procedures do not scale well for larger systems. Our first contribution is a multi-layered ABCD algorithm, where we combine coarse abstractions and lazily computed fine abstractions to improve scalability. So far, we only address reach-avoid and safety specifications, for which our prototype tool (called Mascot) showed up to an order of magnitude speedup on standard benchmark examples. Second, we consider the problem of modular design of sound local controllers for a network of local discrete abstractions communicating via discrete/boolean variables and having local specifications. We propose a sound algorithm, where the systems negotiate a pair of local assume-guarantee contracts, in order to synchronize on a set of non-conflicting and correct behaviors. As a by-product, we also obtain a set of local controllers for the systems which ensure simultaneous satisfaction of the local specifications. We show the effectiveness of the our algorithm using a prototype tool (called Agnes) on a set of discrete benchmark examples. Our third contribution is a novel ABCD algorithm for a more expressive model of nonlinear dynamical systems with stochastic disturbances and ω-regular specifications. This part has two subparts, which are of significant merits on their own rights. First, we present an abstraction algorithm for nonlinear stochastic systems using 2.5-player games (turn-based stochastic graph games). We show that an almost sure winning strategy in this abstract 2.5-player game gives us a sound controller for the original system for satisfying the specification with probability one. Second, we present symbolic algorithms for a seemingly different class of 2-player games with certain environmental fairness assumptions, which can also be used to efficiently compute winning strategies in the aforementioned abstract 2.5-player game. Using our prototype tool (Mascot-SDS), we show that our algorithm significantly outperforms the state-of-the-art implementation on standard benchmark examples from the literature.

Robotic systems are entering the stage. Enabled by advances in both hardware components and software techniques, robots are increasingly able to operate outside of factories, assist humans, and work alongside them. The limiting factor of robots’ expansion remains the programming of robotic systems. Due to the many diverse skills necessary to build a multi-robot system, only the biggest organizations are able to innovate in the space of services provided by robots. To make developing new robotic services easier, in this dissertation I propose a program- ming model in which users (programmers) give a declarative specification of what needs to be accomplished, and then a backend system makes sure that the specification is safely and reliably executed. I present Antlab, one such backend system. Antlab accepts Linear Temporal Logic (LTL) specifications from multiple users and executes them using a set of robots of different capabilities. Building on the experience acquired implementing Antlab, I identify problems arising from the proposed programming model. These problems fall into two broad categories, specification and planning. In the category of specification problems, I solve the problem of inferring an LTL formula from sets of positive and negative example traces, as well as from a set of positive examples only. Building on top of these solutions, I develop a method to help users transfer their intent into a formal specification. The approach taken in this dissertation is combining the intent signals from a single demonstration and a natural language description given by a user. A set of candidate specifications is inferred by encoding the problem as a satisfiability problem for propositional logic. This set is narrowed down to a single specification through interaction with the user; the user approves or declines generated simulations of the robot’s behavior in different situations. In the category of planning problems, I first solve the problem of planning for robots that are currently executing their tasks. In such a situation, it is unclear what to take as the initial state for planning. I solve the problem by considering multiple, speculative initial states. The paths from those states are explored based on a quality function that repeatedly estimates the planning time. The second problem is a problem of reinforcement learning when the reward function is non-Markovian. The proposed solution consists of iteratively learning an automaton representing the reward function and using it to guide the exploration.

Today’s digital world would be unthinkable without complex data sets. Whether in private, business or industrial environments, complex data provide the basis for important and critical decisions and determine many processes, some of which are automated. This is often associated with Big Data. However, often only one aspect of the usual Big Data definitions is sufficient and a human observer can no longer capture the data completely and correctly. In this thesis, different approaches are presented in order to master selected challenges in a more effective, efficient and userfriendly way. The approaches range from easier pre-processing of data sets for later analysis and the identification of design guidelines of such assistants, new visualization techniques for presenting uncertainty, extensions of existing visualizations for categorical data, concepts for time-saving selection methods for subsets of data points and faster navigation and zoom interaction–especially in the web-based area with enormous amounts of data–to new and innovative orientation-based interaction metaphors for mobile devices as well as stationary working environments. Evaluations and appropriate use case of the individual approaches show the usability also in comparison with state-of-the-art techniques.

Sequence learning describes the process of understanding the spatio-temporal relations in a sequence in order to classify it, label its elements or generate new sequences. Due to the prevalence of structured sequences in nature and everyday life, it has many practical applications including any language related processing task. One particular such task that has seen recent success using sequence learning techniques is the optical recognition of characters (OCR). State-of-the-art sequence learning solutions for OCR achieve high performance through supervised training, which requires large amounts of transcribed training data. On the other hand, few solutions have been proposed on how to apply sequence learning in the absence of such data, which is especially common for hard to transcribe historical documents. Rather than solving the unsupervised training problem, research has focused on creating efficient methods for collecting training data through smart annotation tools or generating synthetic training data. These solutions come with various limitations and do not solve all of the related problems. In this work, first the use of erroneous transcriptions for supervised sequence learning is introduced and it is described how this concept can be applied in unsupervised training scenarios by collecting or generating such transcriptions. The proposed OCR pipeline reduces the need of domain specific expertise to apply OCR, with the goal of making it more accessible. Furthermore, an approach for evaluating sequence learning OCR models in the absence of reference transcriptions is presented and its different properties compared to the standard method are discussed. In a second approach, unsupervised OCR is treated as an alignment problem between the latent features of the different language modalities. The outlined solution is to extract language properties from both the text and image domain through adversarial training and learn to align them by adding a cycle consistency constraint. The proposed approach has some strict limitations on the input data, but the results encourage future research into more widespread applications.

In the past, information and knowledge dissemination was relegated to the brick-and-mortar classrooms, newspapers, radio, and television. As these processes were simple and centralized, the models behind them were well understood and so were the empirical methods for optimizing them. In today’s world, the internet and social media has become a powerful tool for information and knowledge dissemination: Wikipedia gets more than 1 million edits per day, Stack Overflow has more than 17 million questions, 25% of US population visits Yahoo! News for articles and discussions, Twitter has more than 60 million active monthly users, and Duolingo has 25 million users learning languages online. These developments have introduced a paradigm shift in the process of dissemination. Not only has the nature of the task moved from being centralized to decentralized, but the developments have also blurred the boundary between the creator and the consumer of the content, i.e., information and knowledge. These changes have made it necessary to develop new models, which are better suited to understanding and analysing the dissemination, and to develop new methods to optimize them. At a broad level, we can view the participation of users in the process of dissemination as falling in one of two settings: collaborative or competitive. In the collaborative setting, the participants work together in crafting knowledge online, e.g., by asking questions and contributing answers, or by discussing news or opinion pieces. In contrast, as competitors, they vie for the attention of their followers on social media. This thesis investigates both these settings. The first part of the thesis focuses on the understanding and analysis of content being created online collaboratively. To this end, I propose models for understanding the complexity of the content of collaborative online discussions by looking exclusively at the signals of agreement and disagreement expressed by the crowd. This leads to a formal notion of complexity of opinions and online discussions. Next, I turn my attention to the participants of the crowd, i.e., the creators and consumers themselves, and propose an intuitive model for both, the evolution of their expertise and the value of the content they collaboratively contribute and learn from on online Q&A based forums. The second part of the thesis explores the competitive setting. It provides methods to help the creators gain more attention from their followers on social media. In particular, I consider the problem of controlling the timing of the posts of users with the aim of maximizing the attention that their posts receive under the idealized setting of full-knowledge of timing of posts of others. To solve it, I develop a general reinforcement learning based method which is shown to have good performance on the when-to-post problem and which can be employed in many other settings as well, e.g., determining the reviewing times for spaced repetition which lead to optimal learning. The last part of the thesis looks at methods for relaxing the idealized assumption of full knowledge. This basic question of determining the visibility of one’s posts on the followers’ feeds becomes difficult to answer on the internet when constantly observing the feeds of all the followers becomes unscalable. I explore the links of this problem to the well-studied problem of web-crawling to update a search engine’s index and provide algorithms with performance guarantees for feed observation policies which minimize the error in the estimate of visibility of one’s posts.