Artificial Intelligence and Cognitive Computing

Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that

Fuzzy systems and unsupervised computing: exploration of applications in biology

In this thesis we will explore the use of fuzzy systems theory for applications in bioinformatics. The theory of fuzzy systems is concerned with formulating decision problems in data sets that are ill-defined. It supports the transfer from a subjective human classification to a numerical scale. In this manner it affords the testing of hypothesis and separation of the classes in the data. We first formulate problems in terms of a fuzzy system and then develop and test algorithms in terms of their performance with data from the domain of the life-sciences. From the results and the performance, we will learn about the usefulness of fuzzy systems for the field, as well as the applicability to the kind of problems and practicality for the computation itself. Computer Systems, Imagery and Medi

Forward uncertainty quantification with special emphasis on a Bayesian active learning perspective

Uncertainty quantification (UQ) in its broadest sense aims at quantitatively studying all sources of uncertainty arising from both computational and real-world applications. Although many subtopics appear in the UQ field, there are typically two major types of UQ problems: forward and inverse uncertainty propagation. The present study focuses on the former, which involves assessing the effects of the input uncertainty in various forms on the output response of a computational model. In total, this thesis reports nine main developments in the context of forward uncertainty propagation, with special emphasis on a Bayesian active learning perspective. The first development is concerned with estimating the extreme value distribution and small first-passage probabilities of uncertain nonlinear structures under stochastic seismic excitations, where a moment-generating function-based mixture distribution approach (MGF-MD) is proposed. As the second development, a triple-engine parallel Bayesian global optimization (T-PBGO) method is presented for interval uncertainty propagation. The third contribution develops a parallel Bayesian quadrature optimization (PBQO) method for estimating the response expectation function, its variable importance and bounds when a computational model is subject to hybrid uncertainties in the form of random variables, parametric probability boxes (p-boxes) and interval models. In the fourth research, of interest is the failure probability function when the inputs of a performance function are characterized by parametric p-boxes. To do so, an active learning augmented probabilistic integration (ALAPI) method is proposed based on offering a partially Bayesian active learning perspective on failure probability estimation, as well as the use of high-dimensional model representation (HDMR) technique. Note that in this work we derive an upper-bound of the posterior variance of the failure probability, which bounds our epistemic uncertainty about the failure probability due to a kind of numerical uncertainty, i.e., discretization error. The fifth contribution further strengthens the previously developed active learning probabilistic integration (ALPI) method in two ways, i.e., enabling the use of parallel computing and enhancing the capability of assessing small failure probabilities. The resulting method is called parallel adaptive Bayesian quadrature (PABQ). The sixth research presents a principled Bayesian failure probability inference (BFPI) framework, where the posterior variance of the failure probability is derived (not in closed form). Besides, we also develop a parallel adaptive-Bayesian failure probability learning (PA-BFPI) method upon the BFPI framework. For the seventh development, we propose a partially Bayesian active learning line sampling (PBAL-LS) method for assessing extremely small failure probabilities, where a partially Bayesian active learning insight is offered for the classical LS method and an upper-bound for the posterior variance of the failure probability is deduced. Following the PBAL-LS method, the eighth contribution finally obtains the expression of the posterior variance of the failure probability in the LS framework, and a Bayesian active learning line sampling (BALLS) method is put forward. The ninth contribution provides another Bayesian active learning alternative, Bayesian active learning line sampling with log-normal process (BAL-LS-LP), to the traditional LS. In this method, the log-normal process prior, instead of a Gaussian process prior, is assumed for the beta function so as to account for the non-negativity constraint. Besides, the approximation error resulting from the root-finding procedure is also taken into consideration. In conclusion, this thesis presents a set of novel computational methods for forward UQ, especially from a Bayesian active learning perspective. The developed methods are expected to enrich our toolbox for forward UQ analysis, and the insights gained can stimulate further studies

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field

Uncertain Multi-Criteria Optimization Problems

Most real-world search and optimization problems naturally involve multiple criteria as objectives. Generally, symmetry, asymmetry, and anti-symmetry are basic characteristics of binary relationships used when modeling optimization problems. Moreover, the notion of symmetry has appeared in many articles about uncertainty theories that are employed in multi-criteria problems. Different solutions may produce trade-offs (conflicting scenarios) among different objectives. A better solution with respect to one objective may compromise other objectives. There are various factors that need to be considered to address the problems in multidisciplinary research, which is critical for the overall sustainability of human development and activity. In this regard, in recent decades, decision-making theory has been the subject of intense research activities due to its wide applications in different areas. The decision-making theory approach has become an important means to provide real-time solutions to uncertainty problems. Theories such as probability theory, fuzzy set theory, type-2 fuzzy set theory, rough set, and uncertainty theory, available in the existing literature, deal with such uncertainties. Nevertheless, the uncertain multi-criteria characteristics in such problems have not yet been explored in depth, and there is much left to be achieved in this direction. Hence, different mathematical models of real-life multi-criteria optimization problems can be developed in various uncertain frameworks with special emphasis on optimization problems

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

Efficient Learning Machines

Computer scienc

Integrated hydrogeological and geochemical processes in swelling clay-sulfate rocks

Quellende Ton-Sulfatgesteine führen immer wieder zu unvorhergesehenen Problemen im Tunnelbau oder bei oberflächennahen Geothermiebohrungen und machen dort langwierige Sanierungsmaßnahmen erforderlich. Die Prozesse, die dem Quellen zugrunde liegen, sind komplex. Im Allgemeinen wird davon ausgegangen, dass der Quellvorgang hauptsächlich auf die Umwandlung von Anhydrit zu Gips zurückzuführen ist. Auslöser ist in der Regel eine Änderung der hydraulischen Bedingungen, gefolgt von einem Wasserzutritt in die quellfähigen Gesteinsschichten, was wiederum die vorherrschenden geochemischen Bedingungen verändert. In der Folge kommt es zu einer Zunahme des Gesteinsvolumens im Untergrund. Dies führte in der süddeutschen Stadt Staufen, dem Untersuchungsstandort dieser Arbeit, zu großräumigen Hebungen an der Geländeoberfläche und, damit verbunden, zu großen Schäden an Häusern und Infrastruktur. Gerade diese hydrogeologischen und geochemischen Prozesse, sowie der Einfluss menschlicher Aktivitäten (z.B. Geothermiebohrungen), lassen sich jedoch nur sehr schwer nachvollziehen oder gar vorhersagen, da die genauen Zusammenhänge bisher unzureichend erforscht sind. Im ersten Teil dieser Arbeit wird zunächst ein 3D geologisches Modell entwickelt, um die komplexen geologischen Verhältnisse im Untersuchungsgebiet zu rekonstruieren. Dieses Modell stellt die geometrische Grundlage für die im weiteren Verlauf durchgeführten numerischen Untersuchungen der hydrogeologischen und geochemischen Prozesse des Quellphänomens dar. In diesem Zusammenhang wird außerdem eine Unsicherheitenanalyse der 3D geologischen Modellierung basierend auf der Theorie der Informationsentropie durchgeführt. Die Analyse veranschaulicht wie sich verschiedene geologische Erkundungsdaten unterschiedlich auf die vorhandenen Modellunsicherheiten und die Modellgeometrie auswirken. Der erstmals auf ein komplexes Standortmodell angewendete Ansatz ermöglicht dabei eine detaillierte, Voxel-basierte Visualisierung und Quantifizierung der Unterschiede und Änderungen der Unsicherheit zwischen mehreren Modellinterpretationen. Zusätzlich können mit Hilfe der verwendeten Jaccard- und der City-block-Distanzen Unähnlichkeiten zwischen den Modellen direkt identifiziert werden. Damit ermöglicht die Methodik unter anderem eine effizientere Durchführung von geologischen Erkundungskampagnen und bietet außerdem eine fundierte Grundlage für Kosten-Nutzen-Analysen. Für die komplexen geologischen Verhältnisse des Untersuchungsstandorts Staufen zeigt sich, dass mit zunehmender Datendichte mehr geologische Strukturen identifiziert werden, gleichzeitig aber auch vermehrt lokal hohe strukturelle Unsicherheiten auftreten. Im zweiten Teil der Arbeit wird ein neuartiger Modellansatz entwickelt und numerisch als radialsymmetrisches, reaktives Transportmodell umgesetzt. Das Model kann genutzt werden, um den Quellprozess abzubilden und berücksichtigt folgende Einflüsse: 1) die veränderten hydraulischen Randbedingungen auf Grund menschlicher Aktivitäten (Geothermiebohrungen), 2) die Wasserverfügbarkeit in der Quellzone, und 3) die Geochemie. Dazu wird die Quellhebung an der Geländeoberfläche in Abhängigkeit der geochemischen Umwandlung von Anhydrit in Gips und einer daraus abgeleiteten Volumenzunahme im Untergrund simuliert und quantifiziert. Der Modellansatz trennt dabei zwischen advektivem Stofftransport entlang von Klüften im Gestein und der Umwandlung von Anhydrit zu Gips in der Gesteinsmatrix. Um den beiden Wirkungsbereichen (Domänen) spezifische Porositäten zuordnen zu können, wird ein Zwei-Domänen Modellierungsansatz (``dual domain approach\u27\u27) verwendet, der diese gleichzeitig über eine Transferrate für den diffusiven Wassertransport koppelt. Mit diesem Modellansatz können prozessspezifische hydraulische, geochemische und mechanische Modellparameter basierend auf geodätischen Hebungsdaten in einer inversen Modellierung abgeschätzt werden. Die hierbei ermittelten Reaktionskonstanten für Anhydritlösung (\SI{2.4e-5}{\mole\per\square\metre\per\second}) und Gipsfällung (\SI{3.2e-6}{\mole\per\square\metre\per\second}) sind vergleichbar mit Literaturwerten aus Laborversuchen. Es zeigt sich jedoch, dass der diffuse Stofftransport in die Gesteinsmatrix wesentlich die Geschwindigkeit des Quellprozesses beeinflusst, was insbesondere bei niedrigen Gesteinsporositäten (z. B. kompakte Anhydritlagen) ein limitierender Faktor sein kann. Insgesamt ist das Modell in der Lage, den am Untersuchungsstandort beobachteten Hebungsverlauf abzubilden. Im dritten Teil der Arbeit wird das zuvor entwickelte Quellhebungsmodell auf die komplexe geologische Situation am Untersuchungsstandort Staufen angewendet. Dadurch können, im Vergleich zum radialsymmetrischen Ansatz, sowohl lokale Grundwasserströmungen, als auch die örtlichen geologischen Gegebenheiten explizit und umfassend bei der Simulation des Quellprozesses berücksichtigt werden. Das Modell kann genutzt werden, um eine Prognose über die weitere Entwicklung der Hebungsprozesse in Abhängigkeit der Sanierungsmaßnahmen vorzunehmen und bietet damit die wissenschaftliche Grundlage für eine Bewertung verschiedener Strategien, um den Quellprozess zu stoppen. Die Methode ermöglicht eine Bilanzierung der Wasserzuflüsse in die Quellzone, sowie eine Abschätzung des zukünftige Quellpotentials für individuelle Sanierungsszenarien. Für den Untersuchungsstandort Staufen zeigen die Ergebnisse, dass auch bei einer unvollständigen, nachträglichen Abdichtung der Erdwärmesonden der Wasserfluss in die Quellzone und damit der Quellprozess durch entsprechende hydraulische Gegenmaßnahmen gestoppt werden kann. Außerdem wird ersichtlich, dass umfassende geologische, hydraulische und geochemische Informationen für eine stichhaltige Simulation der Quellprozesse und eine Beurteilung geeigneter standortspezifischer Sanierungsmaßnahmen erforderlich sind

The Fuzziness in Molecular, Supramolecular, and Systems Chemistry

Fuzzy Logic is a good model for the human ability to compute words. It is based on the theory of fuzzy set. A fuzzy set is different from a classical set because it breaks the Law of the Excluded Middle. In fact, an item may belong to a fuzzy set and its complement at the same time and with the same or different degree of membership. The degree of membership of an item in a fuzzy set can be any real number included between 0 and 1. This property enables us to deal with all those statements of which truths are a matter of degree. Fuzzy logic plays a relevant role in the field of Artificial Intelligence because it enables decision-making in complex situations, where there are many intertwined variables involved. Traditionally, fuzzy logic is implemented through software on a computer or, even better, through analog electronic circuits. Recently, the idea of using molecules and chemical reactions to process fuzzy logic has been promoted. In fact, the molecular word is fuzzy in its essence. The overlapping of quantum states, on the one hand, and the conformational heterogeneity of large molecules, on the other, enable context-specific functions to emerge in response to changing environmental conditions. Moreover, analog input–output relationships, involving not only electrical but also other physical and chemical variables can be exploited to build fuzzy logic systems. The development of “fuzzy chemical systems” is tracing a new path in the field of artificial intelligence. This new path shows that artificially intelligent systems can be implemented not only through software and electronic circuits but also through solutions of properly chosen chemical compounds. The design of chemical artificial intelligent systems and chemical robots promises to have a significant impact on science, medicine, economy, security, and wellbeing. Therefore, it is my great pleasure to announce a Special Issue of Molecules entitled “The Fuzziness in Molecular, Supramolecular, and Systems Chemistry.” All researchers who experience the Fuzziness of the molecular world or use Fuzzy logic to understand Chemical Complex Systems will be interested in this book