Intelligent Based Systems Engineering

The International Council on Systems Engineering (INCOSE) defines Systems Engineering as an interdisciplinary approach and means to enable the realization of successful systems. Researchers are using intelligence-based techniques to support the practices of systems engineering in an innovative way. This research volume includes a selection of contributions by subject experts to design better systems.

The third edition of this bestseller examines the principles of artificial intelligence and their application to engineering and science, as well as techniques for developing intelligent systems to solve practical problems. Covering the full spectrum of intelligent systems techniques, it incorporates knowledge-based systems, computational intelligence, and their hybrids. Using clear and concise language, Intelligent Systems for Engineers and Scientists, Third Edition features updates and improvements throughout all chapters. It includes expanded and separated chapters on genetic algorithms and single-candidate optimization techniques, while the chapter on neural networks now covers spiking networks and a range of recurrent networks. The book also provides extended coverage of fuzzy logic, including type-2 and fuzzy control systems. Example programs using rules and uncertainty are presented in an industry-standard format, so that you can run them yourself. The first part of the book describes key techniques of artificial intelligence—including rule-based systems, Bayesian updating, certainty theory, fuzzy logic (types 1 and 2), frames, objects, agents, symbolic learning, case-based reasoning, genetic algorithms, optimization algorithms, neural networks, hybrids, and the Lisp and Prolog languages. The second part describes a wide range of practical applications in interpretation and diagnosis, design and selection, planning, and control. The author provides sufficient detail to help you develop your own intelligent systems for real applications. Whether you are building intelligent systems or you simply want to know more about them, this book provides you with detailed and up-to-date guidance. Check out the significantly expanded set of free web-based resources that support the book at: http://www.adrianhopgood.com/aitoolkit/

The International Council on Systems Engineering (INCOSE) defines Systems Engineering as an interdisciplinary approach and means to enable the realization of successful systems. Researchers are using intelligence-based techniques to support the practices of systems engineering in an innovative way. This research volume includes a selection of contributions by subject experts to design better systems.

Engineering Intelligent Systems Exploring the three key disciplines of intelligent systems As artificial intelligence (AI) and machine learning technology continue to develop and find new applications, advances in this field have generally been focused on the development of isolated software data analysis systems or of control systems for robots and other devices. By applying model-based systems engineering to AI, however, engineers can design complex systems that rely on AI-based components, resulting in larger, more complex intelligent systems that successfully integrate humans and AI. Engineering Intelligent Systems relies on Dr. Barclay R. Brown’s 25 years of experience in software and systems engineering to propose an integrated perspective to the challenges and opportunities in the use of artificial intelligence to create better technological and business systems. While most recent research on the topic has focused on adapting and improving algorithms and devices, this book puts forth the innovative idea of transforming the systems in our lives, our societies, and our businesses into intelligent systems. At its heart, this book is about how to combine systems engineering and systems thinking with the newest technologies to design increasingly intelligent systems. Engineering Intelligent Systems readers will also find: An introduction to the fields of artificial intelligence with machine learning, model-based systems engineering (MBSE), and systems thinking—the key disciplines for making systems smarter An example of how to build a deep neural network in a spreadsheet, with no code or specialized mathematics required An approach to the visual representation of systems, using techniques from moviemaking, storytelling, visual systems design, and model-based systems engineering An analysis of the potential ability of computers to think, understand and become conscious and its implications for artificial intelligence Tools to allow for easier collaboration and communication among developers and engineers, allowing for better understanding between stakeholders, and creating a faster development cycle A systems thinking approach to people systems—systems that consist only of people and which form the basis for our organizations, communities and society Engineering Intelligent Systems offers an intriguing new approach to making systems more intelligent using artificial intelligence, machine learning, systems thinking, and system modeling and therefore will be of interest to all engineers and business professionals, particularly systems engineers.

The International Council on Systems Engineering (INCOSE) defines Systems Engineering as an interdisciplinary approach and means to enable the realization of successful systems. Researchers are using intelligence-based techniques to support the practices of systems engineering in an innovative way. This research volume includes a selection of contributions by subject experts to design better systems.

In this 2012 edition of Advances in Knowledge-Based and Intelligent Information and Engineering Systems the latest innovations and advances in Intelligent Systems and related areas are presented by leading experts from all over the world. The 228 papers that are included cover a wide range of topics. One emphasis is on Information Processing, which has become a pervasive phenomenon in our civilization. While the majority of Information Processing is becoming intelligent in a very broad sense, major research in Semantics, Artificial Intelligence and Knowledge Engineering supports the domain specific applications that are becoming more and more present in our everyday living. Ontologies play a major role in the development of Knowledge Engineering in various domains, from Semantic Web down to the design of specific Decision Support Systems. Research on Ontologies and their applications is a highly active front of current Computational Intelligence science that is addressed here. Other subjects in this volume are modern Machine Learning, Lattice Computing and Mathematical Morphology.The wide scope and high quality of these contributions clearly show that knowledge engineering is a continuous living and evolving set of technologies aimed at improving the design and understanding of systems and their relations with humans.

Many current AI and machine learning algorithms and data and information fusion processes attempt in software to estimate situations in our complex world of nested feedback loops. Such algorithms and processes must gracefully and efficiently adapt to technical challenges such as data quality induced by these loops, and interdependencies that vary in complexity, space, and time. To realize effective and efficient designs of computational systems, a Systems Engineering perspective may provide a framework for identifying the interrelationships and patterns of change between components rather than static snapshots. We must study cascading interdependencies through this perspective to understand their behavior and to successfully adopt complex system-of-systems in society. This book derives in part from the presentations given at the AAAI 2021 Spring Symposium session on Leveraging Systems Engineering to Realize Synergistic AI / Machine Learning Capabilities. Its 16 chapters offer an emphasis on pragmatic aspects and address topics in systems engineering; AI, machine learning, and reasoning; data and information fusion; intelligent systems; autonomous systems; interdependence and teamwork; human-computer interaction; trust; and resilience.

When men of knowledge impart this knowledge, I do not mean they will convince your reason. I mean they will awaken in you the faith that it is so. - Sri Krishna, Bhagavadgita BACKGROUND The use of computers has led to significant productivity increases in the en gineering industry. Most ofthe computer-aided engineering applications were . restricted to algorithmic computations, such as finite element programs and circuit analysis programs. However, a number ofproblems encountered in en gineering are not amenable to purely algorithmic solutions. These problems are often ill-structured; the term ill-structured problems is used here to de note problems that do not have a clearly defined algorithmic solution. An experienced engineer deals with these ill-structured problems using his/her judgment and experience. The knowledge-based systems (KBS) technology, which emerged out of research in artificial intelligence (AI), offers a method ologyto solve these ill-structuredengineering problems. The emergenceofthe KBS technology can be viewed as the knowledge revolution: other important events that led to increased productivity are the industrial revolution (17th century); the invention of the transistor and associated developments (first half of the 20th century); and the world-wide web (towards the end of the 20th century). Kurzweil, in a lecture at M. LT on December 3, 1987, linked the progress of automation to two industrial revolutions: the first industrial PREFACE xxxii revolution leveraged our physical capabilities, whereas the second industrial revolution - the knowledge revolution - is expected leverage oUr mental ca pabilities.