Applying mechanisms and principles of human intelligence and converging the brain and artificial intelligence (AI) is currently a research trend. The applications of AI in brain simulation are countless. Brain-inspired intelligent systems will improve next-generation information processing by applying theories, techniques, and applications inspired by the information processing principles from the brain. Exploring Future Opportunities of Brain-Inspired Artificial Intelligence focuses on the convergence of AI with brain-inspired intelligence. It presents research on brain-inspired cognitive machines with vision, audition, language processing, and thinking capabilities. Covering topics such as data analysis tools, knowledge representation, and super-resolution, this premier reference source is an essential resource for engineers, developers, computer scientists, students and educators of higher education, librarians, researchers, and academicians.

Affective computing is a nascent field situated at the intersection of artificial intelligence with social and behavioral science. It studies how human emotions are perceived and expressed, which then informs the design of intelligent agents and systems that can either mimic this behavior to improve their intelligence or incorporate such knowledge to effectively understand and communicate with their human collaborators. Affective computing research has recently seen significant advances and is making a critical transformation from exploratory studies to real-world applications in the emerging research area known as applied affective computing. This book offers readers an overview of the state-of-the-art and emerging themes in affective computing, including a comprehensive review of the existing approaches to affective computing systems and social signal processing. It provides in-depth case studies of applied affective computing in various domains, such as social robotics and mental well-being. It also addresses ethical concerns related to affective computing and how to prevent misuse of the technology in research and applications. Further, this book identifies future directions for the field and summarizes a set of guidelines for developing next-generation affective computing systems that are effective, safe, and human-centered. For researchers and practitioners new to affective computing, this book will serve as an introduction to the field to help them in identifying new research topics or developing novel applications. For more experienced researchers and practitioners, the discussions in this book provide guidance for adopting a human-centered design and development approach to advance affective computing.

Professor Judea Pearl won the 2011 Turing Award "for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning." This book contains the original articles that led to the award, as well as other seminal works, divided into four parts: heuristic search, probabilistic reasoning, causality, first period (1988-2001), and causality, recent period (2002-2020). Each of these parts starts with an introduction written by Judea Pearl. The volume also contains original, contributed articles by leading researchers that analyze, extend, or assess the influence of Pearl's work in different fields: from AI, Machine Learning, and Statistics to Cognitive Science, Philosophy, and the Social Sciences. The first part of the volume includes a biography, a transcript of his Turing Award Lecture, two interviews, and a selected bibliography annotated by him.

Big data generates around us constantly from daily business, custom use, engineering, and science activities. Sensory data is collected from the internet of things (IoT) and cyber-physical systems (CPS). Merely storing such a massive amount of data is meaningless, as the key point is to identify, locate, and extract valuable knowledge from big data to forecast and support services. Such extracted valuable knowledge is usually referred to as smart data. It is vital to providing suitable decisions in business, science, and engineering applications. Deep Learning Applications for Cyber-Physical Systems provides researchers a platform to present state-of-the-art innovations, research, and designs while implementing methodological and algorithmic solutions to data processing problems and designing and analyzing evolving trends in health informatics and computer-aided diagnosis in deep learning techniques in context with cyber physical systems. Covering topics such as smart medical systems, intrusion detection systems, and predictive analytics, this text is essential for computer scientists, engineers, practitioners, researchers, students, and academicians, especially those interested in the areas of internet of things, machine learning, deep learning, and cyber-physical systems.

There is a tremendous need for computer scientists, data scientists, and software developers to learn how to develop Socratic problem-solving applications. While the amount of data and information processing has been accelerating, our ability to learn and problem-solve with that data has fallen behind. Meanwhile, problems have become too complex to solve in the workplace without a concerted effort to follow a problem-solving process. This problem-solving process must be able to deal with big and disparate data. Furthermore, it must solve problems that do not have a "rule" to apply in solving them. Moreover, it must deal with ambiguity and help humans use informed judgment to build on previous steps and create new understanding. Computer-based Socratic problem-solving systems answer this need for a problem-solving process using big and disparate data. Furthermore, computer scientists, data scientists, and software developers need the knowledge to develop these systems. Socrates Digital (TM) for Learning and Problem Solving presents the rationale for developing a Socratic problem-solving application. It describes how a computer-based Socratic problem-solving system called Socrates DigitalTM can keep problem-solvers on track, document the outcome of a problem-solving session, and share those results with problem-solvers and larger audiences. In addition, Socrates DigitalTM assists problem-solvers to combine evidence about their quality of reasoning for individual problem-solving steps and their overall confidence in the solution. Socrates DigitalTM also captures, manages, and distributes this knowledge across organizations to improve problem-solving. This book also presents how to build a Socrates DigitalTM system by detailing the four phases of design and development: Understand, Explore, Materialize, and Realize. The details include flow charts and pseudo-code for readers to implement Socrates DigitalTM in a general-purpose programming language. The completion of the design and development process results in a Socrates DigitalTM system that leverages artificial intelligence services from providers that include Apple, Microsoft, Google, IBM, and Amazon. In addition, an appendix provides a demonstration of a no-code implementation of Socrates DigitalTM in Microsoft Power Virtual Agent.

Data is the base for information, information is needed to have knowledge, and knowledge is used to make decisions and manage 21st century businesses and organizations. Thus, it is imperative to remain up to date on the major breakthroughs within the technological arena in order to continually expand and enhance knowledge for the benefit of all institutions. Information Technology Trends for a Global and Interdisciplinary Research Community is a crucial reference source that covers novel and emerging research in the field of information science and technology, specifically focusing on underrepresented technologies and trends that influence and engage the knowledge society. While highlighting topics that include computational thinking, knowledge management, artificial intelligence, and visualization, this book is essential for academicians, researchers, and students with an interest in information management.

Throughout the past decade, the notion of ontologies has influenced research in many application areas including databases, information retrieval, electronic commerce, natural language processing, knowledge management, enterprise systems, systems analysis and design, the Web, and more.Ontology-Based Applications for Enterprise Systems and Knowledge Management provides an opportunity for readers to clearly understand the notion of ontology engineering and the practical aspects of this approach in the domains of two interest areas: Knowledge Management Systems and Enterprise Systems. A perfect reference for researchers, scholars, postgraduate students, and practitioners, this book aims to gather the recent advances and research findings of various topics in ontology use for these application areas.

This new resource presents the principles and applications in the emerging discipline of Activity-Based Intelligence (ABI). This book will define, clarify, and demystify the tradecraft of ABI by providing concise definitions, clear examples, and thoughtful discussion. Concepts, methods, technologies, and applications of ABI have been developed by and for the intelligence community and in this book you will gain an understanding of ABI principles and be able to apply them to activity based intelligence analysis.

Conventional computational methods, and even the latest soft computing paradigms, often fall short in their ability to offer solutions to many real-world problems due to uncertainty, imprecision, and circumstantial data. Hybrid intelligent computing is a paradigm that addresses these issues to a considerable extent. The Handbook of Research on Advanced Research on Hybrid Intelligent Techniques and Applications highlights the latest research on various issues relating to the hybridization of artificial intelligence, practical applications, and best methods for implementation. Focusing on key interdisciplinary computational intelligence research dealing with soft computing techniques, pattern mining, data analysis, and computer vision, this book is relevant to the research needs of academics, IT specialists, and graduate-level students.

The exploitation of theoretical results in knowledge representation, language standardization by W3C and data publication initiatives such as Linked Open Data have given a level of concreteness to the field of ontology research. In light of these recent outcomes, ontology development has also found its way to the forefront, benefiting from years of R&D on development tools. Semi-Automatic Ontology Development: Processes and Resources includes state-of-the-art research results aimed at the automation of ontology development processes and the reuse of external resources becoming a reality, thus being of interest for a wide and diversified community of users. This book provides a thorough overview on the current efforts on this subject and suggests common directions for interested researchers and practitioners.

Database technology can be used for various ends, ranging from promotion of democracy to strengthening of nationalism to shoring up authoritarian regimes through misinformation. Its use affects every layer of society: from individuals to households to local governments, and is a consuming issue in the United States Governments stance on privacy, security, and technology.

Healthcare Information Systems and Informatics: Research and Practices compiles estimable knowledge on the research of information systems and informatics applications in the healthcare industry. This book addresses organizational issues, including technology adoption, diffusion, and acceptance, as well as cost benefits and cost effectiveness, of advancing health information systems and informatics applications as innovative forms of investment in healthcare. Rapidly changing technology and the complexity of its applications make this book an invaluable resource to researchers and practitioners in the healthcare fields.

Before the integration of expert systems in biomedical science, complex problems required human expertise to solve them through conventional procedural methods. Advancements in expert systems allow for knowledge to be extracted when no human expertise is available and increases productivity through quick diagnosis. Expert System Techniques in Biomedical Science Practice is an essential scholarly resource that contains innovative research on the methods by which an expert system is designed to solve complex problems through the automation of decision making through the use of if-then-else rules rather than conventional procedural methods. Featuring coverage on a broad range of topics such as image processing, bio-signals, and cognitive AI, this book is a vital reference source for computer engineers, information technologists, biomedical engineers, data-processing specialists, medical professionals, and industrialists within the fields of biomedical engineering, pervasive computing, and natural language processing.

One aspect of common sense reasoning is reasoning about normal cases, e.g. a physician will first try to interpret symptoms by a common disease, and will take more exotic possibilities only later into account. Such "normality" can be encoded, e.g. by
a relation, where case A is considered more normal than case B. This gives a standard semantics or interpretation to nonmonotonic reasoning (a branch of common sense reasoning), or, more formally, to nonmonotonic logics. We consider in this book the repercussions such normality relations and similar
constructions have on the resulting nonmonotonic logics, i.e. which types of logic are adequate for which kind of relation, etc.
We show in this book that some semantics correspond nicely to some logics, but also that other semantics do not correspond to any logics of the usual form.
Key features:
- provides a coherent picture of several formalisms of nonmonotonic logics.
- gives completeness and incompleteness results for many variants of preferential, distance based, and other semantics.
- gives probably the first systematic investigation of definability preservation and its consequences.
- gives new proof techniques for completeness results.
- is centered on semantics

Intelligent methods are used in distributed environments in countless ways, with examples such as propagation, communication, collaboration, and cooperation. With the abundant purposes for intelligence in distributed systems, it is pertinent for researchers, technicians, and students in various areas of computer science to discover the most current and definitive advances in the field.""Intelligence Integration in Distributed Knowledge Management"" provides recent technologies and practices in intelligence for distributed systems, while covering major aspects of the agent based systems. This book is a must for those striving to enhance their understanding of distributed knowledge management and extend their ideas of cooperation using for numerous real-world problems.

In real management situations, uncertainty is inherently present in decision making. As such, it is increasingly imperative to research and develop new theories and methods of fuzzy sets. Theoretical and Practical Advancements for Fuzzy System Integration is a pivotal reference source for the latest scholarly research on the importance of expressing and measuring fuzziness in order to develop effective and practical decision making models and methods. Featuring coverage on an expansive range of perspectives and topics, such as fuzzy logic control, intuitionistic fuzzy set theory, and defuzzification, this book is ideally designed for academics, professionals, and researchers seeking current research on theoretical frameworks and real-world applications in the area of fuzzy sets and systems.

The most thrilling and challenging intellectual issues of humanity - whether and how a consistent account of reality can be obtained, which field of knowledge is capable to deal with the whole of reality and its formal representation, and how powerful intelligent machines can be brought into existence - are research areas demanding innovative and comprehensive study.""Reality, Universal Ontology and Knowledge Systems: Toward the Intelligent World"" provides cutting-edge research on reality, its nature and fundamental structure, and how it may be effectively represented both by human minds and intelligent machines. Striving to describe a standard world model and universal formal ontology, it offers a uniformly organized human knowledge. It includes powerful reasoning systems, and secure communication interoperability between two species of intelligences, as well as existing human beings and nascent computing reasoning systems promising the profound revolution in human values and ways of life.

The universe is a massive system of systems - for example, ecological systems, social systems, commodity and stock markets. These systems are complex, constantly adapting to their environment, and many are essential to the very existence of human beings. To fully understand these systems, complex adaptive systems research uses systemic inquiry to build multi-level and multidisciplinary representations of reality to study these systems.""Applications of Complex Adaptive Systems"" provides a global view of the most up-to-date research on the strategies, applications, practice, and implications of complex adaptive systems, to better understand the various critical systems that surround human life. Researchers working in the field of complex adaptive systems and related fields such as machine learning and artificial intelligence, multi-agent systems, and data mining, as well as professionals in related applications such as defense, bioinformatics, and sociology will find this book an indispensable, state-of-the-art reference.