We do not perceive the present as it is and in totality, nor do we infer the future from the present with any high degree of dependability, nor yet do we accurately know the consequences of our own actions. In addition, there is a fourth source of error to be taken into account, for we do not execute actions in the precise form in which they are imaged and willed. Frank H. Knight [R4.34, p. 202] The "degree" of certainty of confidence felt in the conclusion after it is reached cannot be ignored, for it is of the greatest practical signi- cance. The action which follows upon an opinion depends as much upon the amount of confidence in that opinion as it does upon fav- ableness of the opinion itself. The ultimate logic, or psychology, of these deliberations is obscure, a part of the scientifically unfathomable mystery of life and mind. Frank H. Knight [R4.34, p. 226-227] With some inaccuracy, description of uncertain consequences can be classified into two categories, those which use exclusively the language of probability distributions and those which call for some other principle, either to replace or supplement.

Addresses real-world challenges in using AI Covers the entire AI process in a holistic manner Explains the technical issues in an easy to use manner Provides real-world examples of AI enablement Addresses the challenges of complex enterprises, coalitions and consortia Avoids the hype, with balanced perspective on benefits and drawbacks of AI

Metaheuristics for Resource Deployment under Uncertainty in Complex Systems analyzes how to set locations for the deployment of resources to incur the best performance at the lowest cost. Resources can be static nodes and moving nodes while services for a specific area or for customers can be provided. Theories of modeling and solution techniques are used with uncertainty taken into account and real-world applications used. The authors present modeling and metaheuristics for solving resource deployment problems under uncertainty while the models deployed are related to stochastic programming, robust optimization, fuzzy programming, risk management, and single/multi-objective optimization. The resources are heterogeneous and can be sensors and actuators providing different tasks. Both separate and cooperative coverage of the resources are analyzed. Previous research has generally dealt with one type of resource and considers static and deterministic problems, so the book breaks new ground in its analysis of cooperative coverage with heterogeneous resources and the uncertain and dynamic properties of these resources using metaheuristics. This book will help researchers, professionals, academics, and graduate students in related areas to better understand the theory and application of resource deployment problems and theories of uncertainty, including problem formulations, assumptions, and solution methods.

- Written by world-leading subject specialist in both sport management and artificial intelligence - Includes interviews with elite sports managers and coaches - Examines the competitive advantages offered by AI to a wide-range of areas including Recruitment, Performance & Tactics, Health & Fitness, Pedagogy, Broadcasting, eSports, Gambling, and Stadium Design

- Includes industrial case studies - Includes chapters on cyber physical systems, machine learning, deep learning, cyber security, robotics, smart manufacturing and predictive analytics - surveys current trends and challenges in metaheuristics and industry 4.0

This is volume 2 of the two-volume Soft Computing and Its Applications. This volume discusses several advanced features of soft computing and hybrid methodologies. This new book essentially contains the advanced features of soft computing and different hybrid methodologies for soft computing. The book contains an abundance of examples and detailed design studies. The tool soft computing can be a landmark paradigm of computation with cognition that directly or indirectly tries to replicate the rationality of human beings. The book explains several advanced features of soft computing, such as cognitive maps, complex valued fuzzy sets and fuzzy logic, quantum fuzzy sets and quantum fuzzy logic, and rough sets and hybrid methods that combine neural net fuzzy logic and genetic algorithms. The book contains several real-life applications to present the utility and potential of soft computing. The book: * Discusses the present state of art of soft computing * Includes the existing application areas of soft computing * Presents original research contributions * Discusses the future scope of work in soft computing The book is unique in that it bridges the gap between theory and practice, and it presents several experimental results on synthetic data and real-life data. The book provides a unified platform for applied scientists and engineers in different fields and industries for the application of soft computing tools in many diverse domains of engineering. This book can be used as a textbook and/or reference book by undergraduate and postgraduate students of many different engineering branches, such as electrical engineering, control engineering, electronics and communication engineering, computer sciences, and information sciences.

Philosophy involves a criticism of scientific knowledge, not from a point of view ultimately different from that of science, but from a point of view less concerned with details and more concerned with the h- mony of the body of special sciences. Here as elsewhere, while the older logic shut out possibilities and imprisoned imagination within the walls of the familiar, the newer logic shows rather what may happen, and refuses to decide as to what must happen. Bertrand Russell At any particular stage in the development of humanity knowledge comes up against limits set by the necessarily limited character of the experience available and the existing means of obtaining knowledge. But humanity advances by overcoming such limits. New experience throws down the limits of old experience; new techniques, new means of obtaining knowledge throw down the limits of old techniques and old means of obtaining knowledge. New limits then once again appear. But there is no more reason to suppose these new limits absolute and final than there was to suppose the old ones absolute and final.

Have over a hundred years of brain research revealed all its secrets? This book is motivated by a realization that cortical structure and behavior can be explained by a synergy of seemingly different mathematical notions: global attractors, which define non-invertible neural firing rate dynamics, random graphs, which define connectivity of neural circuit, and prime numbers, which define the dimension and category of cortical operation. Quantum computation is shown to ratify the main conclusion of the book: loosely connected small neural circuits facilitate higher information storage and processing capacities than highly connected large circuits. While these essentially separate mathematical notions have not been commonly involved in the evolution of neuroscience, they are shown in this book to be strongly inter-related in the cortical arena. Furthermore, neurophysiological experiments, as well as observations of natural behavior and evidence found in medical testing of neurologically impaired patients, are shown to support, and to be supported by the mathematical findings.Related Link(s)

The resurgence of artificial intelligence has been fueled by the availability of the present generation of high-performance computational tools and techniques. This book is designed to provide introductory guidance to artificial intelligence, particularly from the perspective of digital systems engineering. Artificial Intelligence and Digital Systems Engineering provides a general introduction to the origin of AI and covers the wide application areas and software and hardware interfaces. It will prove to be instrumental in helping new users expand their knowledge horizon to the growing market of AI tools, as well as showing how AI is applicable to the development of games, simulation, and consumer products, particularly using artificial neural networks. This book is for the general reader, university students, and instructors of industrial, production, civil, mechanical, and manufacturing engineering. It will also be of interest to managers of technology, projects, business, plants, and operations.

This book is a detailed reference on biomedical applications using Deep Learning. Because Deep Learning is an important actor shaping the future of Artificial Intelligence, its specific and innovative solutions for both medical and biomedical are very critical. This book provides a recent view of research works on essential, and advanced topics. The book offers detailed information on the application of Deep Learning for solving biomedical problems. It focuses on different types of data (i.e. raw data, signal-time series, medical images) to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis, image processing perspectives, and even genomics. It takes the reader through different sides of Deep Learning oriented solutions. The specific and innovative solutions covered in this book for both medical and biomedical applications are critical to scientists, researchers, practitioners, professionals, and educations who are working in the context of the topics.

Recent progress in artificial intelligence (AI) has revolutionized our everyday life. Many AI algorithms have reached human-level performance and AI agents are replacing humans in most professions. It is predicted that this trend will continue and 30% of work activities in 60% of current occupations will be automated. This success, however, is conditioned on availability of huge annotated datasets to training AI models. Data annotation is a time-consuming and expensive task which still is being performed by human workers. Learning efficiently from less data is a next step for making AI more similar to natural intelligence. Transfer learning has been suggested a remedy to relax the need for data annotation. The core idea in transfer learning is to transfer knowledge across similar tasks and use similarities and previously learned knowledge to learn more efficiently. In this book, we provide a brief background on transfer learning and then focus on the idea of transferring knowledge through intermediate embedding spaces. The idea is to couple and relate different learning through embedding spaces that encode task-level relations and similarities. We cover various machine learning scenarios and demonstrate that this idea can be used to overcome challenges of zero-shot learning, few-shot learning, domain adaptation, continual learning, lifelong learning, and collaborative learning.

AI for Digital Warfare explores how the weaponising of artificial intelligence can and will change how warfare is being conducted, and what impact it will have on the corporate world. With artificial intelligence tools becoming increasingly advanced, and in many cases more humanlike, their potential in psychological warfare is being recognised, which means digital warfare can move beyond just shutting down IT systems into more all-encompassing hybrid war strategies.

a short and accessible introduction on AI and Cars written by leading experts

The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the success of this approach has transformed the engineering methods of machine learning in artificial intelligence, the significance of these achievements for the modelling of human learning and representation remains unclear. Deep Learning and Linguistic Representation looks at the application of a variety of deep learning systems to several cognitively interesting NLP tasks. It also considers the extent to which this work illuminates our understanding of the way in which humans acquire and represent linguistic knowledge. Key Features: combines an introduction to deep learning in AI and NLP with current research on Deep Neural Networks in computational linguistics. is self-contained and suitable for teaching in computer science, AI, and cognitive science courses; it does not assume extensive technical training in these areas. provides a compact guide to work on state of the art systems that are producing a revolution across a range of difficult natural language tasks.

The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the success of this approach has transformed the engineering methods of machine learning in artificial intelligence, the significance of these achievements for the modelling of human learning and representation remains unclear. Deep Learning and Linguistic Representation looks at the application of a variety of deep learning systems to several cognitively interesting NLP tasks. It also considers the extent to which this work illuminates our understanding of the way in which humans acquire and represent linguistic knowledge. Key Features: combines an introduction to deep learning in AI and NLP with current research on Deep Neural Networks in computational linguistics. is self-contained and suitable for teaching in computer science, AI, and cognitive science courses; it does not assume extensive technical training in these areas. provides a compact guide to work on state of the art systems that are producing a revolution across a range of difficult natural language tasks.

This book covers the issues related to optimization of engineering and management problems using soft computing techniques with an industrial outlook. It covers a broad area related to real life complex decision making problems using a heuristics approach. It also explores a wide perspective and future directions in industrial engineering research on a global platform/scenario. The book highlights the concept of optimization, presents various soft computing techniques, offers sample problems, and discusses related software programs complete with illustrations. Features Explains the concept of optimization and relevance to soft computing techniques towards optimal solution in engineering and management Presents various soft computing techniques Offers problems and their optimization using various soft computing techniques Discusses related software programs, with illustrations Provides a step-by-step tutorial on how to handle relevant software for obtaining the optimal solution to various engineering problems

This volume links the concept of granular computing using deep learning and the Internet of Things to object tracking for video analysis. It describes how uncertainties, involved in the task of video processing, could be handled in rough set theoretic granular computing frameworks. Issues such as object tracking from videos in constrained situations, occlusion/overlapping handling, measuring of the reliability of tracking methods, object recognition and linguistic interpretation in video scenes, and event prediction from videos, are the addressed in this volume. The book also looks at ways to reduce data dependency in the context of unsupervised (without manual interaction/ labeled data/ prior information) training.This book may be used both as a textbook and reference book for graduate students and researchers in computer science, electrical engineering, system science, data science, and information technology, and is recommended for both students and practitioners working in computer vision, machine learning, video analytics, image analytics, artificial intelligence, system design, rough set theory, granular computing, and soft computing.

The aim of this book is to handle different application problems of science and engineering using expert Artificial Neural Network (ANN). As such, the book starts with basics of ANN along with different mathematical preliminaries with respect to algebraic equations. Then it addresses ANN based methods for solving different algebraic equations viz. polynomial equations, diophantine equations, transcendental equations, system of linear and nonlinear equations, eigenvalue problems etc. which are the basic equations to handle the application problems mentioned in the content of the book. Although there exist various methods to handle these problems, but sometimes those may be problem dependent and may fail to give a converge solution with particular discretization. Accordingly, ANN based methods have been addressed here to solve these problems. Detail ANN architecture with step by step procedure and algorithm have been included. Different example problems are solved with respect to various application and mathematical problems. Convergence plots and/or convergence tables of the solutions are depicted to show the efficacy of these methods. It is worth mentioning that various application problems viz. Bakery problem, Power electronics applications, Pole placement, Electrical Network Analysis, Structural engineering problem etc. have been solved using the ANN based methods.

Metaheuristic optimization is a higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity. This is usually applied when two or more objectives are to be optimized simultaneously. This book is presented with two major objectives. Firstly, it features chapters by eminent researchers in the field providing the readers about the current status of the subject. Secondly, algorithm-based optimization or advanced optimization techniques, which are applied to mostly non-engineering problems, are applied to engineering problems. This book will also serve as an aid to both research and industry. Usage of these methodologies would enable the improvement in engineering and manufacturing technology and support an organization in this era of low product life cycle. Features: Covers the application of recent and new algorithms Focuses on the development aspects such as including surrogate modeling, parallelization, game theory, and hybridization Presents the advances of engineering applications for both single-objective and multi-objective optimization problems Offers recent developments from a variety of engineering fields Discusses Optimization using Evolutionary Algorithms and Metaheuristics applications in engineering

This intriguing book was born out of the many discussions the authors had in the past 10 years about the role of scale-free structure and dynamics in producing intelligent behavior in brains. The microscopic dynamics of neural networks is well described by the prevailing paradigm based in a narrow interpretation of the neuron doctrine. This book broadens the doctrine by incorporating the dynamics of neural fields, as first revealed by modeling with differential equations (K-sets). The book broadens that approach by application of random graph theory (neuropercolation). The book concludes with diverse commentaries that exemplify the wide range of mathematical/conceptual approaches to neural fields. This book is intended for researchers, postdocs, and graduate students, who see the limitations of network theory and seek a beachhead from which to embark on mesoscopic and macroscopic neurodynamics.

In this insightful work, Dogaru proposes a systematic framework for measuring emergence and a systematic design method to locate computationally meaningful genes in a reasonable computing time. Programs and application examples are provided so that the reader may easily understand the new concepts and develop her own specific experiments. The book 's approachability recommends it to a large audience including specialists from various interdisciplinary fields.

Businesses today are faced with a highly competitive market and fast-changing technologies. In order to meet demanding customers' needs, they rely on high quality software. A new field of study, soft computing techniques, is needed to estimate the efforts invested in component-based software. Component-Based Systems: Estimating Efforts Using Soft Computing Techniques is an important resource that uses computer-based models for estimating efforts of software. It provides an overview of component-based software engineering, while addressing uncertainty involved in effort estimation and expert opinions. This book will also instruct the reader how to develop mathematical models. This book is an excellent source of information for students and researchers to learn soft computing models, their applications in software management, and will help software developers, managers, and those in the industry to apply soft computing techniques to estimate efforts.

We create technology enabling us to do things never before possible and it ultimately changes the way we live, work, play, and interact with each other. Throughout human history, the democratization of technology making a technology available to the masses, has brought about sweeping cultural, social, political, and societal changes. In the last half-century, the democratization of computers, information, the Internet, and social media have revolutionized and transformed our lives. We now stand at the beginning of a new era sure to bring about waves of new revolutions, the cognitive systems era. Until now, humans have done all of the thinking. However, our lives are about to be infused with artificial entities capable of performing high-level cognitive processing previously possible only in the human mind. Systems capable of this kind of "synthetic cognition" will achieve and surpass the level of human experts in almost every field of endeavor. Far from replacing humans, these cognitive systems will be our collaborators, teachers, confidants, colleagues, and companions. The future will belong to those who can better partner with these cognitive systems. Made available to the average person via the Internet, handheld devices, and through ordinary objects all around us, expertise will become democratized. Everything will change when anyone has access to expertise in any field and new things will be possible. The democratization of expertise is the foundation on which our society's revolutions will be built over the next half-century. This book discusses societal and cultural revolutions throughout history brought about by the adoption of new technology and gives brief histories of human cognitive augmentation and artificial intelligence. In the coming cognitive systems era, humans, by collaboratively partnering with cognitive systems, will together achieve expert-level performance-synthetic expertise-with humans performing some of the cognitive processing and cognitive systems performing some. As the capabilities of cognitive systems improve over time, the balance of thinking will shift from being mostly human to mostly artificial. This book introduces the Levels of Cognitive Augmentation to describe this shift. Drawing from previous research in cognitive systems and intelligent agent theory, the knowledge stores required for expertise are identified in a Knowledge Level description of expertise. This book introduces a new abstract level, called the Expertise Level to describe the skills needed for expertise. Combining the knowledge-level and expertise-level descriptions, this book introduces the Model of Expertise. This book demonstrates use of the Model of Expertise by presenting several synthetic expert architectures: a synthetic teacher (Synthia), a synthetic friend/therapist (Sy), a synthetic elderly companion (Lois), a synthetic research companion (Synclair), and an automated scientific hypothesis explorer (Ashe). This book is intended for anyone interested in the fields of cognitive systems, cognitive computing, cognitive augmentation, or artificial intelligence or the impact of technologies from these fields on society. Anyone doing research and development in the area of cognitive systems or artificial intelligence will find this book particularly useful.

We create technology enabling us to do things never before possible and it ultimately changes the way we live, work, play, and interact with each other. Throughout human history, the democratization of technology making a technology available to the masses, has brought about sweeping cultural, social, political, and societal changes. In the last half-century, the democratization of computers, information, the Internet, and social media have revolutionized and transformed our lives. We now stand at the beginning of a new era sure to bring about waves of new revolutions, the cognitive systems era. Until now, humans have done all of the thinking. However, our lives are about to be infused with artificial entities capable of performing high-level cognitive processing previously possible only in the human mind. Systems capable of this kind of "synthetic cognition" will achieve and surpass the level of human experts in almost every field of endeavor. Far from replacing humans, these cognitive systems will be our collaborators, teachers, confidants, colleagues, and companions. The future will belong to those who can better partner with these cognitive systems. Made available to the average person via the Internet, handheld devices, and through ordinary objects all around us, expertise will become democratized. Everything will change when anyone has access to expertise in any field and new things will be possible. The democratization of expertise is the foundation on which our society's revolutions will be built over the next half-century. This book discusses societal and cultural revolutions throughout history brought about by the adoption of new technology and gives brief histories of human cognitive augmentation and artificial intelligence. In the coming cognitive systems era, humans, by collaboratively partnering with cognitive systems, will together achieve expert-level performance-synthetic expertise-with humans performing some of the cognitive processing and cognitive systems performing some. As the capabilities of cognitive systems improve over time, the balance of thinking will shift from being mostly human to mostly artificial. This book introduces the Levels of Cognitive Augmentation to describe this shift. Drawing from previous research in cognitive systems and intelligent agent theory, the knowledge stores required for expertise are identified in a Knowledge Level description of expertise. This book introduces a new abstract level, called the Expertise Level to describe the skills needed for expertise. Combining the knowledge-level and expertise-level descriptions, this book introduces the Model of Expertise. This book demonstrates use of the Model of Expertise by presenting several synthetic expert architectures: a synthetic teacher (Synthia), a synthetic friend/therapist (Sy), a synthetic elderly companion (Lois), a synthetic research companion (Synclair), and an automated scientific hypothesis explorer (Ashe). This book is intended for anyone interested in the fields of cognitive systems, cognitive computing, cognitive augmentation, or artificial intelligence or the impact of technologies from these fields on society. Anyone doing research and development in the area of cognitive systems or artificial intelligence will find this book particularly useful.

Businesses today are faced with a highly competitive market and fast-changing technologies. In order to meet demanding customers' needs, they rely on high quality software. A new field of study, soft computing techniques, is needed to estimate the efforts invested in component-based software. Component-Based Systems: Estimating Efforts Using Soft Computing Techniques is an important resource that uses computer-based models for estimating efforts of software. It provides an overview of component-based software engineering, while addressing uncertainty involved in effort estimation and expert opinions. This book will also instruct the reader how to develop mathematical models. This book is an excellent source of information for students and researchers to learn soft computing models, their applications in software management, and will help software developers, managers, and those in the industry to apply soft computing techniques to estimate efforts.