Our time recognizes robustness as an important, all-pervading feature in the world around us. Despite its omnipresence, robustness is not entirely understood, rather dif?cult to de?ne, and, despite its obvious value in many situations, rather dif?cult to achieve. One of the goals of this edited book is to report on the topic of robustness from a variety and diverse range of ?elds and perspectives. We are interested, for instance, in fundamental strategies nature applies to make systems robust—and arguably “intelligent”—and how these strategies may hold as general design principles in modern technology. A particular focus is on computer-based systems and appli- tions. This in mind, the book has four main sections: Part I has a look at robustness in terms of underlying technologies and infrastr- tures upon which many computer-based “intelligent” systems reside and inves- gates robustness on the hardware and software level, but also in larger environments such as the Internet and self-managing systems. The contributions in Part II target robustness in research areas that are inspired by biology, including brain-computer interfaces, biological networks, and biological immune systems, for example. Part III involves the exciting ?eld of arti?cial intelligence. The chapters here discuss the value of robustness as a general design principle for arti?cial intelligence, stressing its potential in areas such as humanoid robotics and image processing.

This volume explores the intersection of robust intelligence (RI) and trust in autonomous systems across multiple contexts among autonomous hybrid systems, where hybrids are arbitrary combinations of humans, machines and robots. To better understand the relationships between artificial intelligence (AI) and RI in a way that promotes trust between autonomous systems and human users, this book explores the underlying theory, mathematics, computational models, and field applications. It uniquely unifies the fields of RI and trust and frames it in a broader context, namely the effective integration of human-autonomous systems. A description of the current state of the art in RI and trust introduces the research work in this area. With this foundation, the chapters further elaborate on key research areas and gaps that are at the heart of effective human-systems integration, including workload management, human computer interfaces, team integration and performance, advanced analytics, behavior modeling, training, and, lastly, test and evaluation. Written by international leading researchers from across the field of autonomous systems research, Robust Intelligence and Trust in Autonomous Systems dedicates itself to thoroughly examining the challenges and trends of systems that exhibit RI, the fundamental implications of RI in developing trusted relationships with present and future autonomous systems, and the effective human systems integration that must result for trust to be sustained. Contributing authors: David W. Aha, Jenny Burke, Joseph Coyne, M.L. Cummings, Munjal Desai, Michael Drinkwater, Jill L. Drury, Michael W. Floyd, Fei Gao, Vladimir Gontar, Ayanna M. Howard, Mo Jamshidi, W.F. Lawless, Kapil Madathil, Ranjeev Mittu, Arezou Moussavi, Gari Palmer, Paul Robinette, Behzad Sadrfaridpour, Hamed Saeidi, Kristin E. Schaefer, Anne Selwyn, Ciara Sibley, Donald A. Sofge, Erin Solovey, Aaron Steinfeld, Barney Tannahill, Gavin Taylor, Alan R. Wagner, Yue Wang, Holly A. Yanco, Dan Zwillinger.

This book is a collection of some of the papers that were presented during a NATO Advanced Research Workshop (ARW) on "Intelligent Systems: Safety, Reliability and Maintainability Issues" that was held in Kusadasi, Turkey during August 24- 28, 1992. Attendance at this workshop was mainly by invitation only, drawing people internationally representing industry, government and the academic community. Many of the participants were internationally recognized leaders in the topic of the workshop. The purpose of the ARW was to bring together a highly distinguished group of people with the express purpose of debating where the issues of safety, reliability and maintainability place direct and tangible constraints on the development of intelligent systems. As a consequence, one of the major debating points in the ARW was the definition of intelligence, intelligent behaviour and their relation to complex dynamic systems. Two major conclusions evolved from the ARW are: 1. A continued need exists to develop formal, theoretical frameworks for the architecture of such systems, together with a reflection on the concept of intelligence. 2. There is a need to focus greater attention to the role that the human play in controlling intelligent systems. The workshop began by considering the typical features of an intelligent system. The complexity associated with multi-resolutional architectures was then discussed, leading to the identification of a necessity for the use of a combinatorial synthesis/approach. This was followed by a session on human interface issues.

Computational intelligence is a well-established paradigm, where new theories with a sound biological understanding have been evolving. The current experimental systems have many of the characteristics of biological computers (brains in other words) and are beginning to be built to perform a variety of tasks that are difficult or impossible to do with conventional computers. As evident, the ultimate achievement in this field would be to mimic or exceed human cognitive capabilities including reasoning, recognition, creativity, emotions, understanding, learning and so on. This book comprising of 17 chapters offers a step-by-step introduction (in a chronological order) to the various modern computational intelligence tools used in practical problem solving. Staring with different search techniques including informed and uninformed search, heuristic search, minmax, alpha-beta pruning methods, evolutionary algorithms and swarm intelligent techniques; the authors illustrate the design of knowledge-based systems and advanced expert systems, which incorporate uncertainty and fuzziness. Machine learning algorithms including decision trees and artificial neural networks are presented and finally the fundamentals of hybrid intelligent systems are also depicted. Academics, scientists as well as engineers engaged in research, development and application of computational intelligence techniques, machine learning and data mining would find the comprehensive coverage of this book invaluable.

The Industrial Electronics Handbook, Second Edition combines traditional and newer, more specialized knowledge that will help industrial electronics engineers develop practical solutions for the design and implementation of high-power applications. Embracing the broad technological scope of the field, this collection explores fundamental areas, including analog and digital circuits, electronics, electromagnetic machines, signal processing, and industrial control and communications systems. It also facilitates the use of intelligent systems—such as neural networks, fuzzy systems, and evolutionary methods—in terms of a hierarchical structure that makes factory control and supervision more efficient by addressing the needs of all production components. Enhancing its value, this fully updated collection presents research and global trends as published in the IEEE Transactions on Industrial Electronics Journal, one of the largest and most respected publications in the field. As intelligent systems continue to replace and sometimes outperform human intelligence in decision-making processes, they have made substantial contributions to the solution of very complex problems. As a result, the field of computational intelligence has branched out in several directions. For instance, artificial neural networks can learn how to classify patterns, such as images or sequences of events, and effectively model complex nonlinear systems. Simple and easy to implement, fuzzy systems can be applied to successful modeling and system control. Illustrating how these and other tools help engineers model nonlinear system behavior, determine and evaluate system parameters, and ensure overall system control, Intelligent Systems: Addresses various aspects of neural networks and fuzzy systems Focuses on system optimization, covering new techniques such as evolutionary methods, swarm, and ant colony optimizations Discusses several applications that deal with methods of computational intelligence Other volumes in the set: Fundamentals of Industrial Electronics Power Electronics and Motor Drives Control and Mechatronics Industrial Communication Systems

It is widely anticipated that autonomous vehicles will have a transformational impact on military forces and will play a key role in many future force structures. As a result, many tasks have already been identified that unmanned systems could undertake more readily than humans. However, for this to occur, such systems will need to be agile, versatile, persistent, reliable, survivable and lethal. This will require many of the vehicles ‘cognitive’ or higher order functions to be more fully developed, whereas to date only the ‘component’ or physical functions have been successfully automated and deployed. The book draws upon a broad range of others’ work with a view to providing a product that is greater than the sum of its parts. The discussion is intentionally approached from the perspective of improving understanding rather than providing solutions or drawing firm conclusions. Consequently, researchers reading this book with the hope of uncovering some novel theory or approach to automating an unmanned vehicle will be as disappointed as the capability planner who anticipates a catalogue of technical risks and feasibility options against his favoured list of component technologies and potential applications. Nevertheless, it is hoped that both will at least learn something of the other’s world and that progress will ensue as a result. For the defence policy and decision maker, this is a "must-read" book which brings together an important technology summary with a considered analysis of future doctrinal, legal and ethical issues in unmanned and autonomous systems. For research engineers and developers of robotics, this book provides a unique perspective on the implications and consequences of our craft; connecting what we do to the deployment and use of the technology in current and future defence systems. Professor Hugh Durrant-Whyte

Hybrid architecture for intelligent systems is a new field of artificial intelligence concerned with the development of the next generation of intelligent systems. This volume is the first book to delineate current research interests in hybrid architectures for intelligent systems. The book is divided into two parts. The first part is devoted to the theory, methodologies, and algorithms of intelligent hybrid systems. The second part examines current applications of intelligent hybrid systems in areas such as data analysis, pattern classification and recognition, intelligent robot control, medical diagnosis, architecture, wastewater treatment, and flexible manufacturing systems. Hybrid Architectures for Intelligent Systems is an important reference for computer scientists and electrical engineers involved with artificial intelligence, neural networks, parallel processing, robotics, and systems architecture.