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• PREFACE.
• 1. INTELLIGENCE IN NATURAL AND CONSTRUCTED SYSTEMS. 1
• .1. Introduction. 1
• .2. Brief Overview of the Evolving Concepts of Mind and Intelligence. 1
• .3. Intelligent Systems: Can We Distinguish Them from Nonintelligent Systems? 1
• .4. Intelligence: Product and Tool of Behavior and Communication. 1
• .5. Evolution of Automatisms. 1
• .6. From Agent to Multiscale Communities of Agents. 1
• .7. Cognitive Agents and Architectures. References. Problems.
• 2. THEORETICAL FUNDAMENTALS. 2
• .1. Mathematical Framework of the Architectures for Intelligent Systems. 2
• .2. Formal Model of Intelligent Systems and Processes. 2
• .3. Necessary Terminology and Assumptions. 2
• .4. Construction and Properties of Objects. 2
• .5. Extracting Entities from Reality. 2
• .6. Grouping + Filtering + Search: The Elementary Unit of Self--Organization. 2
• .7. Relative Intelligence and Its Evolution. 2
• .8. On the Resemblances among Processes of Structuring in Nature and Representation. 2
• .9. Semiotic Framework of the Architectures for Intelligent Systems. References. Problems.
• 3. KNOWLEDGE REPRESENTATION. 3
• .1. Problem of Representing the Natural World. 3
• .2. What Is Knowledge? 3
• .3. Knowledge Representation in the Brain: Acquiring Automatisms. 3
• .4. Sensory and Symbolic Representations in the Brain. 3
• .5. Reference Frame, Imagination, and Insight. 3
• .6. Principles of Knowledge Representation, Entities, and Relational Structures. 3
• .7. Multiresolutional Character of Knowledge and Its Complexity. 3
• .8. Virtual Phenomena of Knowledge Representation. References. Problems.
• 4. REFERENCE ARCHITECTURE. 4
• .1. Components of a Reference Architecture. 4
• .2. Evolution of the Reference Architecture for Intelligent Systems. 4
• .3. Hierarchy with Horizontal "In Level" Connections. 4
• .4. Levels of Resolution. 4
• .5. Neural Components of the Architecture. 4
• .6. Behavior--Generating Hierarchy. 4
• .7. Analysis of Multiresolutional Architectures. 4
• .8. Agent--Based Reference Architectures. References. Problems.
• 5. MOTIVATIONS, GOALS, AND VALUE JUDGMENT. 5
• .1. Internal Needs versus External Goals. 5
• .2. Value Judgments. 5
• .3. Achieving the Goal: Optimization via the Calculus of Variations. References. Problems.
• 6. SENSORY PROCESSING. 6
• .1. In--level and Inter--level Processes. 6
• .2. Sensory Processing as a Module of the Level. 6
• .3. Hierarchy of Sensory Processing. 6
• .4. Multiresolutional Nature of Sensory Processing. References. Problems.
• 7. BEHAVIOR GENERATION. 7
• .1. Preliminary Concepts of Multiresolutional Behavior Generation. 7
• .2. BG Architecture. 7
• .3. Strategy of Multiresolutional Control: Generation of a Nested Hierarchy. 7
• .4. Overall Organization of Behavior Generation. 7
• .5. PLANNER. 7
• .6. EXECUTOR: Its Structure and Functioning. 7
• .7. Conclusions: Integrating BG in the Intelligent System. References. Problems.
• 8. MULTIRESOLUTIONAL PLANNING: A SKETCH OF THE THEORY. 8
• .1. Introduction to Planning. 8
• .2. Emerging Problems in Planning. 8
• .3. Planning of Actions and Planning of States. 8
• .4. Linkage between Planning and Learning. 8
• .5. Planning in Architectures of Behavior Generation. 8
• .6. Path Planning in a Multidimensional Space. 8
• .7. Multiresolutional Planning as a Tool of Increasing Efficiency of Behavior Generation. References. Problems.
• 9. MULTIRESOLUTIONAL HIERARCHY OF PLANNER/EXECUTOR MODULES. 9
• .1. Hybrid Control Hierarchy. 9
• .2. Theoretical Premises. 9
• .3. Canonical Hybrid PLANNER/EXECUTOR Module. 9
• .4. Quasi--minimum Time Functioning of PLANNER/EXECUTOR Modules Equipped with a Production System. 9
• .5. Approximate Inverse via Forward Searching. 9
• .6. Multirate Hierarchical Predictive Control System. 9
• .7. Conclusion. References. Problems.
• 10. LEARNING. 10
• .1. Intelligent Systems and Learning. 10
• .2. Definitions of Learning. 10
• .3. Implicit and Explicit Logical and Psychological Schemes of Learning. 10
• .4. Information Acquisition via Learning: Domains of Application. 10
• .5. Axiomatic Theory of Learning Control Systems. 10
• .6. Learning and Behavior Generation: Constructing and Using MR Representation and Goal Hierarchies. 10
• .7. Baby--Robot: Analysis of Early Cognitive Development. 10
• .8. Applying Neural Networks for Learning. References. Problems.
• 11. APPLICATIONS OF MULTIRESOLUTIONAL ARCHITECTURES FOR INTELLIGENT SYSTEMS. 11
• .1. An Intelligent Systems Architecture for Manufacturing. 11
• .2. Planning in the Hierarchy of NIST--RCS for Manufacturing. 11
• .3. Inspection Workstation--Based Testbed Application for the Intelligent Systems Architecture for Manufacturing. 11
• .4. RCS--Based RoboCrane Integration. 11
• .5. Mobile Robot Goes Multiresolutional. 11
• .6. Mission Structure for an Unmanned Vehicle. 11
• .7. 4--D/RCS as an Implementation Guide for Demo III. References.
• 12. INTELLIGENT SYSTEMS: PRECURSOR OF THE NEW PARADIGM IN SCIENCE AND ENGINEERING. 12
• .1. Multidisciplinarity: The Most Promising But a Bumpy Road. 12
• .2. What Is the Core Activity of Intelligent Systems? 12
• .3. More Questions Than We Can Handle. 12
• .4. Multiresolutional Recursive Sign Processing with Learning (MRSPL Processing). 12
• .5. Multiresolutional Intelligence. 12
• .6. Learning How to Know: Semiotics and Multiscale Cybernetics. 12
• .7. Engineering of Mind. References. ABBREVIATIONS. NAME INDEX. SUBJECT INDEX.
• (source: Nielsen Book Data)

This comprehensive treatment of the field of intelligent systems is written by two of the foremost authorities in the field. The authors clearly examine the theoretical and practical aspects of these systems. The book focuses on the NIST-RCS (Real-time Control System) model, that has been used recently in the Mars Rover.
(source: Nielsen Book Data)

• Acknowledgements. Preface.
• 1. Getting Started.
• 2. Knowledge Representation.
• 3. Reasoning and Search in Rule-Based Systems.
• 4. Verification and Validation of Rule-Bases.
• 5. Tools for Representation and Reasoning.
• 6. Real-Time Expert Systems.
• 7. Qualitative Reasoning.
• 8. Petri Nets.
• 9. Fuzzy Control Systems.
• 10.
• G2: An Example of a Real-Time Expert System. Appendices. References. Index. About the Authors.
• (source: Nielsen Book Data)

Intelligent control is a rapidly developing, complex and challenging field with great practical importance and potential. Because of the rapidly developing and interdisciplinary nature of the subject, there are only a few edited volumes consisting of research papers on intelligent control systems but little is known and published about the fundamentals and the general know-how in designing, implementing and operating intelligent control systems. Intelligent control system emerged from artificial intelligence and computer controlled systems as an interdisciplinary field. Therefore, the book summarizes the fundamentals of knowledge representation, reasoning, expert systems and real-time control systems and then discusses the design, implementation verification and operation of real-time expert systems using G2 as an example. Special tools and techniques applied in intelligent control are also described including qualitative modelling, Petri nets and fuzzy controllers. The material is illustrated with simple examples taken from the field of intelligent process control. Audience: The book is suitable for advanced undergraduate students and graduate engineering students. In addition, practicing engineers will find it appropriate for self-study.
(source: Nielsen Book Data)

• Part 1: general concepts, architectures and technologies
• intelligent autonomous robotic systems - some general issues and two representative research prototypes, S.G. Tzafestas
• increased autonomy in industrial robotic systems - a framework, G. Bolmsjo, et al
• combining visual, tactile and range sensor information to increase the autonomy of robots, M. Seitz, et al
• the ICA approach to intelligent autonomous systems, R. Sanz, et al
• intelligent robotic agent combining reactive and cognitive capabilities, W. Jacak, S. Dreiseitl
• Reactive Planning with Learned Operational Concepts
• V. Klingspor
• Sensors Used for Autonomous Navigation
• E.M. Nebot. Part 2: Mobile, Walking and Snake-Like Robots
• Design, Control and Applications of Autonomous Mobile Robots
• D. Floreano, et al
• Intelligent Autonomous Systems: Visual Navigation Functionalities for Autonomous Mobile Vehicles
• E. Stella, A. Distante
• Mapping and Navigating in Time-Varying Obstacle Fields
• R. Jarvis
• High Integrity Navigation for Autonomous Systems
• E.M. Nebot
• Autonomous Robot Motion Planning and Control in Uncertain Environments: Overview and a New Algorithm Based on Sliding-Mode Control
• S.G. Tzafestas, et al
• Autonomous Control for an Omnidirectional Mobile Robot with Feedback Control System
• K. Watanabe, et al
• On the Design of a Control Architecture for an Autonomous Mobile Robot
• E. Pereira da Silva, et al
• Dynamic Walking: A Step Forward? R.E. Reeve
• An Omnidirectional Control Algorithm for Walking Machines Based on a Wave-Crab Gait
• M.A. Jimenez, et al
• Dynamic Modeling and Locomotion Control of Biped Autonomous Robots
• T.E. Krikochoritis, et al
• A Novel Robotic Snake
• K.J. Kyriakopoulos, et al. Part 3: Applications
• Autonomous Agents in Cellular Manufacturing
• E.S. Tzafestas
• Man-Machine Cooperation for the Control of an Intelligent Powered Wheelchair
• G. Bourhis, Y. Agostini
• The Design, Development and Operation of an Autonomous Robotic Excavator
• D.A. Bradley, D.W. Seward
• Autonomous Robotic Exploration and Gaze Planning Using Range Sensing
• P.A.M. Renton, et al
• Mixed Traffic Control Involving Manually-Controlled and Automatically-Controlled Vehicles in IVHS
• S.-N. Huang, et al.
• (source: Nielsen Book Data)

This book presents a collection of twenty-three timely contributions that cover a well-selected repertory of topics within the autonomous systems field. Intelligent autonomous systems (IAS) are finding new applications in industrial production, health-care service and other sectors of modern life. The book is divided into the following three parts: Part 1 - General Concepts, Architectures and Technologies, Part 2 - Mobile, Walking and Snake-Like Robots, and Part 3 - Applications.Part 1 contains seven chapters dealing with the requirements, architectures and implementation(s) of intelligent/sensor-based autonomous systems and robots. Part 2 presents eleven contributions covering the design, path-planning, navigation, and motion-planning and control of mobile robots including legged robots and snake-like robots. Finally, Part 3 involves five important applications, namely: cellular manufacturing, robotic wheelchairs, robotic excavators, robotic explorers and autonomously controlled vehicles on the highway. The book can serve as a reference for the young and senior researcher and practitioner in the field. A wide repertory of IAS design, construction, control and operation problems can be found along with a multiplicity of well-established and novel solutions.
(source: Nielsen Book Data)

• Introduction.- High-Order Iterative Learning Control of Uncertain Nonlinear Systems with State Delays.- High-Order P-type Iterative Learning Controller Using Current Iteration Tracking Error.- Iterative Learning Control of Uncertain Nonlinear Discrete-time Systems Using Current Iteration Tracking Error.- Iterative Learning Control of Uncertain Nonlinear Discrete-time Feedback Systems with Saturation.- Initial State Learning Method for Iterative Learning Control of Uncertain Time-varying Systems.- High-order Terminal Iterative Learning Control with An application to a Rapid Thermal Process for Chemical Vapor Deposition.- Designing Iterative Learning Controller via Noncausal Filtering.- High Precision Iterative Learning Control of Permanent Magnetic Linear Motor with Less Modeling Effort.- Iterative Learning Identification with An Application to Aerodynamic Drag Coefficient Curve Extraction Problem.- Iterative Learning Control of Functional Neuromuscular Stimulation Systems.- Conclusions and Future Research.- References.- Index.
• (source: Nielsen Book Data)

This is an overview of iterative learning control. It can be used as a text or reference for a course at graduate level and is also suitable for self-study and for industry-oriented courses of continuing education. Ranging from aerodynamic curve identification robotics to functional neuromuscular stimulation, Iterative Learning Control (ILC), which started in the early 1980s, is found to have wide applications in practice. Generally, a system under control may have uncertainties in its dynamic model and its environment. One attractive point in ILC lies in the utilization of the system repetitiveness to reduce such uncertainties and in turn to improve the control performance by operating the system repeatedly. This monograph emphasises both theoretical and practical aspects of ILC, and provides some developments in ILC convergence and robustness analysis. The book also considers issues in ILC design. Several practical applications are presented to illustrate the effectiveness of ILC. The applied examples provided in this monograph are particularly beneficial to readers who wish to capitalise the system repetitiveness to improve system control performance.
(source: Nielsen Book Data)

• Fundamentals of intelligent control
• methodology of knowledge representation
• general search principles
• advanced reasoning systems
• hierarchical control systems
• expert control systems
• learning control systems
• automatic planning systems
• intelligent control systems in application
• prospect of intelligent control.
• (source: Nielsen Book Data)

This book introduces the development process, structural theories and research areas of intelligent control; explains the knowledge representations, searching and reasoning mechanisms as the fundamental techniques of intelligent control; studies the theoretical principles and architectures of various intelligent control systems; analyzes the paradigms of representative applications of intelligent control; and discusses the research and development trends of the intelligent control. From the general point of view, this book possesses the following features: updated research results both in theory and application that reflect the latest advances in intelligent control; closed connection between theory and practice that enables readers to use the principles to their case studies and practical projects; and comprehensive materials that helps readers in understanding and learning.
(source: Nielsen Book Data)

• Part 1: Intelligent Control Surveys. 1. Introduction: Overview of Intelligent Controls
• S.G. Tzafestas. 2. Fuzzy and Neural Intelligent Control: Basic Principles and Architectures
• S.G. Tzafestas, C.S. Tzafestas. 3. Intelligent Control Using Artificial Neural Networks and Fuzzy Logic - Recent Trends and Industrial Applications
• A. Zilouchian, et al. 4. Control of Robotic Manipulators Using Neural Networks - A Survey
• P. Gupta, N.K. Sinha. Part 2: Intelligent Control Methods. 5. Intelligent Process Control with Supervisory Knowledge-Based Systems
• S.J. Kendra, et al. 6. Fuzzy Model Based Predictive Controller
• C. Batur, et al. 7. Fuzzy Adaptive Control Versus Model-Reference Adaptive Control of Mutable Processes
• I. Skrjanc, D. Matko. 8. Intelligent Control and Supervision Based on Fuzzy Petri Nets
• G.K.H. Pang, et al. 9. Off-Line Verification of an Intelligent Control
• V.S. Alagar, K. Periyasamy. 10. Intelligent Neurofuzzy Estimators and Multisensor Data Fusion
• C.J. Harris, et al. 11. Linguistic Communication Channels
• W. Pedrycz. Part 3: Intelligent Control Applications. 12. Intelligent Human-Machine Systems
• G. Johannsen, et al. 13. Intelligent Control for a Robotic Acrobat
• S.C. Brown, K.M. Passino. 14. Adaptive and Learning Control of Robotic Manipulators
• Z. Ahmad, A. Guez. 15. Fuzzy Evolutionary Algorithms and Automatic Robot Trajectory Generation
• B.H. Xu, et al. 16. Application of Intelligent Control Techniques in Advanced AGC
• K. Liu, et al. 17. Manufacturing Controller Design and Deadlock Avoidance Using a Matrix Model for Discrete Event Systems
• F.L. Lewis, et al. 18. Adaptive, Model-Based Predictive, and Neural Control of the Welding Process
• S.G. Tzafestas, et al. Biographies of the Contributors.
• (source: Nielsen Book Data)

The field of Intelligent Control has expanded rapidly in recent years with many theoretical and practical results that go beyond those of classical controls. This book presents a collection of timely contributions that cover a well- selected range of topics within the field, including the latest issues of human-machine systems, linguistic communication systems, and automatic verification of intelligent control software.The book contains eighteen chapters written by forty-four experts, and is divided into the following three parts: Part 1: Intelligent Control Surveys; Part 2: Intelligent Control Methods; and, Part 3: Intelligent Control Applications. Part 1 contains four chapters providing general overviews of intelligent control, fuzzy and neural control, and their industrial applications. Part 2 presents a set of important methods with emphasis on neural, fuzzy and neurofuzzy control concepts and techniques. Finally, Part 3 deals with the application of intelligent control to robotic manipulators, automatic power generation control, manufacturing systems, and welding processes. The book can serve as a reference volume for the researcher and practitioner in the field, and as a rich information source for the teacher and student in related postgraduate programs. Readers can thus save considerable time in searching the scattered literature in the field.
(source: Nielsen Book Data)

• Review on the design of intelligent control systems
• learning stochastic automata
• stochastic grammars
• hierarchical intelligent machines revistied
• a peformance measure for intelligent control systems
• the intelligent machine as a hierarchical stochastic automation
• design methodology and execution algorithm
• convergence rate and convergence acceleration of learning
• examples and cases studies
• conclusions and suggested future work.
• (source: Nielsen Book Data)

In recent years works done by most researchers towards building autonomous intelligent controllers frequently mention the need for a methodology of design and a measure of how successful the final result is. This monograph introduces a design methodology for intelligent controllers based on the analytic theory of intelligent machines introduced by Saridis in the 1970s. The methodology relies on the existing knowledge about designing the different subsystems composing an intelligent machine. Its goal is to provide a measure of the performance applicable to any of the subsystems, and use that measure to learn on-line the best among the set of pre-designed alternatives, given the state of the environment where the machine operates. Different designs can be compared using this novel approach.
(source: Nielsen Book Data)

• Review of previous work
• qualitative bong graph modelling
• hybrid qualitative and quantitative control
• auto-tuning
• fault diagnosis
• intelligent supervisory control
• conclusions and future work.
• (source: Nielsen Book Data)

In this book, a knowledge representation and utilization methodology integrating qualitative reasoning and bond graphs is developed to construct intelligent supervisory control systems. Qualitative reasoning is a powerful model-based reasoning method while bond graphs are a formal modelling language of dynamic systems. Their integration and qualitative reasoning on bond graphs results in a problem-solving approach in artificial intelligence, in which qualitative reasoning is used as the general reasoning strategy and bond graphs are employed as the knowledge representation. A systematic modelling procedure based on qualitative bond graphs is presented. A controller design method is developed to derive control algorithms from qualitative bond graph models. An auto-tuning scheme is proposed to adjust the controllers in order to meet performance criteria and adapt to system changes. A fault diagnosis mechanism is built to localize system faults, and an additional measurement suggestion method is developed for the diagnosis result refinement. An automatic planner is proposed to generate the operation sequences for system start-up, shut-down, and emergency measures to help human operators operate systems safely. All of these applications are combined together via a management mechanism to construct a supervisory control system.
(source: Nielsen Book Data)

• Partial table of contents: Intelligent Hybrid Systems: Issues, Classifications and Future Directions (S. Goonatilake & S. Khebbal). FUNCTION-REPLACING HYBRIDS. Neural Network Weight Selection Using Genetic Algorithms (D. Montana). INTERCOMMUNICATING HYBRIDS. A Hybrid System for Data Mining (R. Kerber, et al.). A Multi-Agent Approach for the Integration of Neural Networks and Expert Systems (A. Scherer & G. Schlageter). POLYMORPHIC HYBRIDS. Reasoning with Rules and Variables in Neural Networks (V. Ajjanagadde & L. Shastri). The NeurOagent: A Neural Multi-Agent Approach for Modelling, Distributed Processing and Learning (K. Pham). DEVELOPING HYBRID SYSTEMS. Tools and Environments for Hybrid Systems (S. Khebbal & D. Shamhong). Glossary. Intelligent Systems Resource Guide. Index.
• (source: Nielsen Book Data)

There is now a growing realisation in the intelligent systems community that many complex problems require hybrid solutions. Increasingly, hybrid systems combining genetic algorithms, fuzzy logic, neural networks and expert systems are proving their effectiveness in a wide variety of real-world problems. This timely book brings together leading researchers from the United States, Europe and Asia who are pioneering the theory and application of Intelligent Hybrid Systems. The book provides a definition of hybrid systems, summarises the current state of the art, and presents contributions that detail innovative methods for integrating different intelligent techniques. Application examples are drawn from domains including industrial control, financial and business modelling, and cognitive simulation. The book is also intended to equip researchers, application developers and managers with key references and resource material for the successful development of hybrid systems.
(source: Nielsen Book Data)

• Part 1 Theory and architectures: introduction to intelligent control systems with high degrees of autonomy, P.A. Antsaklis and K.M. Passino
• a reference model architecture for intelligent systems design, J.S. Albus
• model-based architecture concepts for autonomous systems design and simulation, B.P. Zeigler and Sungdo Chi
• design of structure-based hierarchies for distributed intelligent control, L. Acar and U. Ozguner
• modelling and design of distributed intelligence systems, A.H. Levis
• nested hierarchical control, A. Meystel. Part 2 Design approaches and techniques: expert control, K.J. Astrom and K.-E. Arzen
• modelling and analysis of artificially intelligent planning systems, K.M. Passino and P.J. Antsaklis
• fuzzy and neural control, H.R. Berenji
• learning control systems, J.A. Farrell and W. Baker
• learning control methods, needs and architectures, M.M. Kokar
• learning in control, E. Grant. Part 3 Applications: intellingent robot prehension, Thang N. Nguyen and H. Stephanou
• modelling of multi-sensory robotic systems with failure diagnostic capabilities, K.P. Valavanis and G. Seetharaman
• AUTOCREW - a paradigm for intelligent flight control, B.L. Belkin and R.F. Stengel
• a framework for knowledge-based diagnosis in process operations, P.R. Prased and J.F. Davis.
• (source: Nielsen Book Data)

The area of intelligent control is a fusion of a number of research areas in engineering computer science and mathematics, which has evolved from conventional control to enhance the existing nonlinear, optimal, adaptive and stochastic control methods. Intelligent control techniques are currently being utilized for closed-loop feedback control in space-based applications, manufacturing systems, robotic systems, avionic systems, among others, to improve system performance, reliability and efficiency. Overall, the primary objective of intelligent control is to enhance the performance of the system to the extent that it achieves some level of autonomous control. This work provides an introduction to, and survey of, the vital and emerging area of intelligent control by leading researchers in the area. Contributors to "An Introduction to Intelligent and Autonomous Control" are world-wide experts who have been invited on the strength of their research. The fundamental theory, archictectures and perspectives on intelligent control are presented. Approaches to intelligent control, including expert control, planning systems, fuzzy control, neural control and learning control are studied in detail. Applications are introduced via robotic systems, avionic systems and failure diagnosis for process operations. "An Introduction to Intelligent and Autonomous Control" is designed as a reference for professionals and academic researchers and may also be used as the foundation for graduate level courses on intelligent and autonomous control.
(source: Nielsen Book Data)

After a long period, in which the research focused mainly on industrial robotics, nowadays scientists aim to build machines able to act autonomously in unstructured domains, and to interface friendly with humans, while performing intelligently their assigned tasks. Such intelligent autonomous systems are now being intensively developed, and are ready to be applied to every field, from social life to modern enterprises. We believe the following years will be increasingly characterised by their extensive use. This is dramatically changing the whole scenario of human society. The aim of IAS-6 has been to develop a technical programme with the highest quality of scientific and technical contents, while offering the participants the maximum possibility of interaction in order to facilitate the exchange of ideas and laboratory experience.
(source: Nielsen Book Data)