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• Front Cover; Foundations of the Theory of Learning Systems; Copyright Page; Contents; Preface to the Russian Edition; Acknowledgments;
• Chapter I. Goal of Learning;
• Chapter II. Algorithms of Learning;
• Chapter III. Algorithms of Optimal Learning;
• Chapter IV. Elements of Statistical Decision Theory;
• Chapter V. Learning Pattern Recognition Systems;
• Chapter VI. Self-Learning Systems of Classification;
• Chapter VII. Learning Models;
• Chapter VIII. Learning Filters;
• Chapter IX. Examples of Learning Systems; Epilogue; Author Index; Subject Index.

Foundations of the theory of learning systems.

• Front Cover; Foundations of the Theory of Learning Systems; Copyright Page; Contents; Preface to the Russian Edition; Acknowledgments;
• Chapter I. Goal of Learning;
• Chapter II. Algorithms of Learning;
• Chapter III. Algorithms of Optimal Learning;
• Chapter IV. Elements of Statistical Decision Theory;
• Chapter V. Learning Pattern Recognition Systems;
• Chapter VI. Self-Learning Systems of Classification;
• Chapter VII. Learning Models;
• Chapter VIII. Learning Filters;
• Chapter IX. Examples of Learning Systems; Epilogue; Author Index; Subject Index.

Foundations of the theory of learning systems.

• Part I: Introduction.- Design versus Self-organization.- Foundations and Formalizations of Self-Organization.- Part II: Distributed Management and Control.- Self-organizing Traffic Lights: A Realistic Simulation.- A Self-organizing Sensing System for Structural Health Monitoring of Aerospace Vehicles.- Decentralized Decision Making for Multi-Agent Systems.- Learning Mutation Strategies for Evolution and Adaptation of a Simulated Snakebot.- Self-Organization as Phase Transition in Decentralized Groups of Robots.- Distributed Control of Microscopic Robots in Biomedical Applications.- Part III: Self-organizing Computation.- Self-Organizing Digital Systems.- Self-organizing Nomadic Services in Grids.- Immune System Support for Scheduling.- Formal Immune Networks.- A Model for Self-organizing Data Visualization Using Decentralized Multi-Agent Systems.- Emergence of Travelling Localizations in Mutualistic-Excitation Media.- Part IV: Discussion.- A Turing Test for Emergence.- Index.
• (source: Nielsen Book Data)

The main challenge faced by designers of self-organizing systems is how to validate and control non-deterministic dynamics. Over-engineering the system may completely suppress self-organization with an outside influence, eliminating emergent patterns and decreasing robustness, adaptability and scalability. Whilst leaving too much non-determinism in the system??'s behaviour may make its verification and validation almost impossible. This book presents the state-of-the-practice in successfully engineered self-organizing systems, and examines ways to balance design and self organization in the context of applications. As demonstrated throughout, finding this balance helps to deal with diverse practical challenges. The book begins with the more established fields of traffic management and structural health monitoring, building up towards robotic teams, solving challenging tasks deployed in tough environments. The second half of the book follows with a deeper look into the micro-level, and considers local interactions between agents. These interactions lead towards self-modifying digital circuitry and self-managing grids, self-organizing data visualization and intrusion detection in computer networks, immunocomputing and nature-inspired computation, and eventually to artificial life. The case studies described illustrate the richness of the topic and provide guidance to its intricate areas. Many algorithms proposed and discussed in this volume are biologically inspired and readers will also gain an insight into cellular automata, genetic algorithms, artificial immune systems, snake-like locomotion, ant foraging, birds flocking and mutualistic biological ecosystems, amongstothers. Demonstrating the practical relevance and applicability of self-organization, this book will be of interest to advanced students and researchers in a wide range of fields.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the First International Workshop on Self-Organizing Systems, IWSOS 2006, held in Passau, Germany in September 2006. The 16 revised full papers and 6 revised short papers presented together with 2 invited talks and 3 poster papers were carefully selected from more than 70 submissions. The papers are organized in topical sections on dynamics of structured and unstructured overlays, self-organization in peer-to-peer networks, self-organization in wireless environments, self-organization in distributed and grid computing, self-organization for network management and routing, self-managing and autonomic computing, peer-to-peer systems, as well as self-protection and security.
(source: Nielsen Book Data)

• Preface. Introduction. Part One: Physics of Autosolitons and Phenomena of Self-Organization in Active Systems. 1. 'Ball Lightning' in Semiconductors and Gases. 2. Regions of High or Low Electron Temperature in Heated Semiconductor and Gas Plasmas. 3. Traveling Pulses and Other Autowaves in Excitable Media. 4. Static, Traveling, and Pulsating Autosolitons. 5. Current Filaments in Systems with Single-Valued Voltage-Current Characteristic. 6. Static and Traveling Strata in Solids and Gases. 7. Hot Spots in Semiconductors and Semiconductor Structures. 8. Autosolitons in Other Active Media. 9. Classification of Active Distributed Media. 10. Classification of Autosolitons and Phenomena of Self-Organization. Part Two: Theory of Autosolitons. 11. Static Autosolitons in One-Dimensional Media (KN and KI-Systems). 12. Stability and Evolution of Static Autosolitons in One-Dimensional Media (KN and Ki-Systems). 13. Static Autosolitons in Two and Three-Dimensional Media (KN and KI-Systems). 14. Theory of Strata: Interacting One-Dimensional Autosolitons (KN and KI-Systems). 15. Spike Static Autosolitons and Strata (KLambda and KV-Systems). 16. Pulsating Autosolitons (KOmega-Systems). 17. Traveling Autosolitons and Autowaves (KOmega and Omega-Systems). 18. Autosolitons in Bistable (Trigger) Systems. Part Three: Scenarios of Self-Organization and Turbulence in Active Distributed Media. 19. Structures near Stratification Point of Homogeneous State of the System. 20. Effects Definitive for Rearrangment of Autosolitons and Strata (K-Systems). 21. Scenarios ofSelf-Organization in Ideally Homogeneous One-Dimensional Systems. 22. Scenarios of Self-Organization in Real One-Dimensional Systems. 23. Self-organization in Two and Three-Dimensional Systems. 24. Turbulence in Active Systems. Conclusion.
• Appendix 1: Asymptotic Theory of Static Autosolitons and Strata.
• Appendix 2: Analytical Investigation of Autosolitons and Strata in an Axiomatic Model of Active Medium with Diffusion. References.
• (source: Nielsen Book Data)

This monograph is devoted to an entirely new branch of nonlinear physics - solitary intrinsic states, or autosolitons, which form in a broad class of physical, chemical and biological dissipative systems. Autosolitons are often observed as highly nonequilibrium regions in slightly nonequilibrium systems, in many ways resembling ball lightning which occurs in the atmosphere. We develop a new approach to problems of self-organization and turbulence, treating these phenomena as a result of spontaneous formation and subsequent evolution of autosolitons. Scenarios of self-organization involve sophisticated interactions between autosolitons, whereas turbulence is regarded as a pattern of autosolitons which appear and disappear at random in different parts of the system. This monograph is the first attempt to provide a comprehensive summary of the theory of autosolitons as developed by the authors over the years of research. The monograph is comprised of three more or less autonomous parts. Part I deals with the physical nature and experimental studies of autosolitons and self- organization in various physical systems: semiconductor and gas plasma, heated gas mixture, semiconductor structures, composite superconductors, optical and magnetic media, systems with uniformly generated combustion matter, distributed gas-discharge and electronic systems. We discuss feasibility of autosolitons in the form of highly nonequilibrium regions in slightly nonequilibrium gases and semiconductors, "hot" and "cold" regions in semiconductor and gas plasmas, static, pulsating and traveling combustion fronts.
(source: Nielsen Book Data)

• Prologue 7 1. Introduction 10 1.1 Autonomous Systems 13 1.2 The Role of Machine Learning in Autonomous Systems 15 1.3 System Identification 20 1.3.3 Novelty detection, outliers and the link to structure innovation 20 1.4 On-line versus Off-line Identification 21 1.5 Adaptive and Evolving Systems 22 1.6 Evolving or Evolutionary Systems 23 1.7 Supervised versus Un-supervised Learning 25 1.8 Structure of the Book 26 PART I: Fundamentals 29 2. Fundamentals of Probability Theory 29 2.1 Randomness and Determinism 30 2.2 Frequentistic versus Belief-based Approach 33 2.3 Probability Densities and Moments 33 2.4 Density Estimation
• Kernel-based Approach 37 2.5 Recursive Density Estimation (RDE) 40 2.6 Detecting Novelties/Anomalies/Outliers using RDE 45 2.7 Conclusion 49 3. Fundamentals of Machine Learning and Pattern Recognition 51 3.1 Pre-processing 51 3.1.1 Normalisation and standardisation 52 3.1.2 Orthogonalization of inputs/features
• rPCA method 53 3.2 Clustering 56 3.2.1 Proximity measures and clusters shape 59 3.2.2 Off-line methods 60 3.2.3 Evolving clustering methods 65 3.3 Classification 73 3.3.1 Recursive LDA, rLDA 74 3.4 Conclusion 74 4. Fundamentals of Fuzzy Systems Theory 77 4.1 Fuzzy Sets 77 4.2 Fuzzy Systems, Fuzzy Rules 80 4.2.1 Fuzzy Systems of Zadeh-Mamdani Type 81 4.2.2 Takagi-Sugeno Fuzzy Systems 83 4.3 Fuzzy Systems with Non-parametric Antecedents (AnYa) 87 4.3.1 Architecture 87 4.3.2 Analysis of AnYa 90 4.4 FRB (Off-line) classifiers 91 4.5 Neuro-Fuzzy Systems 94 4.5.1 Neuro-fuzzy system architecture 94 4.5.2 Evolving NFS as a framework for autonomous learning and knowledge extraction from data streams 97 4.5.3 Linguistic interpretation of the NFS 98 4.6 State Space Perspective 99 4.7 Conclusions 100 Part II Methodology of Autonomous Learning Systems 101 5 Evolving System Structure from Streaming Data 101 5.1 Defining system structure based on prior knowledge 101 5.2 Data Space Partitioning 102 5.2.1 Regular partitioning of the data space 103 5.2.2 Data space partitioning through clustering 104 5.2.3. Data space partitioning based on data clouds 105 5.2.4. Importance of partitioning the joint input-output data space 105 5.2.5 Principles of data space partitioning for autonomous machine learning 107 5.2.6 Dynamic data space partitioning
• evolving system structure autonomously 108 5.3 Normalisation and Standardisation of Streaming Data in Evolving Environments 114 5.3.1 Standardization in an Evolving Environment 115 5.3.2 Normalisation in an Evolving Environment 116 5.4 Autonomous Monitoring of the Structure Quality 117 5.4.1 Autonomous Input Variables Selection 117 5.4.2 Autonomous Monitoring of the Age of the Local Sub-model 120 5.4.3 Autonomous Monitoring of the Utility of the Local Sub-model 122 5.4.4 Update of the Cluster Radii 123 5.5 Short- and Long-term Focal Points and Sub-models 124 5.6 Simplification and Interpretability Issues 125 5.7 Conclusion 127 6 Autonomous Learning Parameters of the Local Sub-models 129 6.1 Learning Parameters of Local Sub-models 130 6.2 Global versus Local Learning 131 6.3 Evolving Systems Structure Recursively 133 6.4 Learning Modes 137 6.5 Robustness to Outliers in Autonomous Learning 140 6.6 Conclusions 140 7 Autonomous Predictors, Estimators, Filters, Inferential Sensors 142 7.1 Predictors, Estimators, Filters
• Problem Formulation 142 7.2 Non-linear Regression 144 7.3 Time series 145 7.4 Autonomous Learning Sensors 146 7.4.1 Autonomous Sensors
• Problem Definition 146 7.4.2 A brief Overview of Soft/Intelligent/Inferential Sensors 147 7.4.3 Autonomous Intelligent Sensors (AutoSense) 149 7.4.4 AutoSense Architecture 151 7.4.5 Modes of Operation of AutoSense 152 7.4.6 Autonomous Input Variable Selection 152 7.5 Conclusions 153 8. Autonomous Learning Classifiers 155 8.1 Classifying data streams 155 8.2 Why adapt the classifier structure? 155 8.3 Architecture of Autonomous Classifiers of the Family AutoClassify 157 8.3.1 AutoClassify0 159 8.3.2 AutoClassify1 159 8.4 Learning AutoClassify from Streaming Data 162 8.4.1 Learning AutoClassify0 162 8.4.2 Learning AutoClassify1 163 8.5 Analysis of AutoClassify methods 163 8.6 Conclusions 164 9. Autonomous Learning Controllers 166 9.1 Indirect Adaptive Control Scheme 167 9.2 Evolving Inverse Plant Model from On-line Streaming Data 169 9.3 Evolving Fuzzy Controller Structure from On-line Streaming Data 170 9.4 Examples of using AutoControl 172 9.5 Conclusion 177 10. Collaborative Autonomous Learning Systems 179 10.1 Distributed Intelligence Scenarios 179 10.2 Autonomous Collaborative Learning 181 10.3 Collaborative Autonomous Clustering, AutoCluster by a team of ALSs 183 10.4 Collaborative Autonomous Predictors, Estimators, Filters and AutoSense by a team of ALSs 184 10.5 Collaborative Autonomous Classifiers AutoClassify by a team of ALSs 184 10.6 Superposition of Local Sub-models 185 10.7 Conclusion 186 PART III: Applications of ALS 187 11. Autonomous Learning Sensors for Chemical and Petro-chemical Industries 187 11.1 Case Study 1: Quality of the Products in an Oil Refinery 187 11.1.1 Introduction 187 11.1.2 The current state of the art 188 11.1.3 Problem description 189 11.1.4 The data set 189 11.1.5 AutoSesnse for kerosene quality prediction 191 11.1.6 AutoSense for Abel inflammability test 193 11.2 Case Study 2: Polypropylene Manufacturing 194 11.2.1 Problem description 194 11.2.2 Drift and shift detection by cluster age derivatives 198 11.2.3 Input variables selection 200 11.3 Conclusion 201 12. Autonomous Learning Systems in Mobile Robotics 203 12.1 The mobile robot Pioneer 3DX 203 12.2 Autonomous Classifier for Landmark Recognition 205 12.2.1 Corner detection and simple mapping of an indoor environment through wall following 207 12.2.2 Outdoor landmark detection based on visual input information 210 12.2.3 VideoDiaries 214 12.2.4 Collaborative scenario 217 12.3 Autonomous Leader Follower 220 12.4 Results Analysis 223 13. Autonomous Novelty Detection and Object Tracking in Video Streams 224 13.1 Problem Definition 224 13.2 Background subtraction and KDE for detecting visual novelties 225 13.2.1 Background subtraction method 225 13.2.2 Challenges 226 13.2.3 Parametric versus non-parametric approaches 229 13.2.4 Kernel Density Estimation method 230 13.3 Detecting Visual novelties with RDE Method 231 13.4 Object Identification in Image Frames using RDE 232 13.5 Real-time Tracking in Video Streams using ALS 234 13.6 Conclusion 237 14. Modelling Evolving User Behaviour with ALS 239 14.1 User Behaviour as an evolving phenomenon 239 14.2 Designing the User Behaviour Profile 241 14.3 Applying AutoClassify0 for modelling evolving user behaviour 244 14.4 Case studies 245 14.4.1 Users of UNIX commands 245 14.4.2 Modelling activity of people in a smart home environment 247 14.4.3 Automatic scene recognition 249 14.5 Conclusions 252 15. Epilogue 254 15.1 Conclusions 254 15.2 Open Problems 258 15.3 Future Directions 259 Bibliography 261 Index 274 Glossary 291 Appendices 295 A. Mathematical Foundations 296 A1 Probability distributions 297 A2 Basic matrix properties 300 B. Pseudo-code of the basic algorithms 302 B1 Mean shift with Epanechnikov kernel 302 B2 AutoCluster 304 B3 ELM 305 B4 AutoPredict 307 B5 AutoSense 308 B6 AutoClassify0 309 B7 AutoClassify1 311 B8 AutoControl 313.
• (source: Nielsen Book Data)

• Forewords xi Preface xix About the Author xxiii 1 Introduction 1 1.1 Autonomous Systems 3 1.2 The Role of Machine Learning in Autonomous Systems 4 1.3 System Identification an Abstract Model of the Real World 6 1.4 Online versus Offline Identification 9 1.5 Adaptive and Evolving Systems 10 1.6 Evolving or Evolutionary Systems 11 1.7 Supervised versus Unsupervised Learning 13 1.8 Structure of the Book 14 PART I FUNDAMENTALS 2 Fundamentals of Probability Theory 19 2.1 Randomness and Determinism 20 2.2 Frequentistic versus Belief-Based Approach 22 2.3 Probability Densities and Moments 23 2.4 Density Estimation Kernel-Based Approach 26 2.5 Recursive Density Estimation (RDE) 28 2.6 Detecting Novelties/Anomalies/Outliers using RDE 32 2.7 Conclusions 36 3 Fundamentals of Machine Learning and Pattern Recognition 37 3.1 Preprocessing 37 3.2 Clustering 42 3.3 Classification 56 3.4 Conclusions 58 4 Fundamentals of Fuzzy Systems Theory 61 4.1 Fuzzy Sets 61 4.2 Fuzzy Systems, Fuzzy Rules 64 4.3 Fuzzy Systems with Nonparametric Antecedents (AnYa) 69 4.4 FRB (Offline) Classifiers 73 4.5 Neurofuzzy Systems 75 4.6 State Space Perspective 79 4.7 Conclusions 81 PART II METHODOLOGY OF AUTONOMOUS LEARNING SYSTEMS 5 Evolving System Structure from Streaming Data 85 5.1 Defining System Structure Based on Prior Knowledge 85 5.2 Data Space Partitioning 86 5.3 Normalisation and Standardisation of Streaming Data in an Evolving Environment 96 5.4 Autonomous Monitoring of the Structure Quality 98 5.5 Short- and Long-Term Focal Points and Submodels 104 5.6 Simplification and Interpretability Issues 105 5.7 Conclusions 107 6 Autonomous Learning Parameters of the Local Submodels 109 6.1 Learning Parameters of Local Submodels 110 6.2 Global versus Local Learning 111 6.3 Evolving Systems Structure Recursively 113 6.4 Learning Modes 116 6.5 Robustness to Outliers in Autonomous Learning 118 6.6 Conclusions 118 7 Autonomous Predictors, Estimators, Filters, Inferential Sensors 121 7.1 Predictors, Estimators, Filters Problem Formulation 121 7.2 Nonlinear Regression 123 7.3 Time Series 124 7.4 Autonomous Learning Sensors 125 7.5 Conclusions 131 8 Autonomous Learning Classifiers 133 8.1 Classifying Data Streams 133 8.2 Why Adapt the Classifier Structure? 134 8.3 Architecture of Autonomous Classifiers of the Family AutoClassify 135 8.4 Learning AutoClassify from Streaming Data 139 8.5 Analysis of AutoClassify 140 8.6 Conclusions 140 9 Autonomous Learning Controllers 143 9.1 Indirect Adaptive Control Scheme 144 9.2 Evolving Inverse Plant Model from Online Streaming Data 145 9.3 Evolving Fuzzy Controller Structure from Online Streaming Data 147 9.4 Examples of Using AutoControl 148 9.5 Conclusions 153 10 Collaborative Autonomous Learning Systems 155 10.1 Distributed Intelligence Scenarios 155 10.2 Autonomous Collaborative Learning 157 10.3 Collaborative Autonomous Clustering, AutoCluster by a Team of ALSs 158 10.4 Collaborative Autonomous Predictors, Estimators, Filters and AutoSense by a Team of ALSs 159 10.5 Collaborative Autonomous Classifiers AutoClassify by a Team of ALSs 160 10.6 Superposition of Local Submodels 161 10.7 Conclusions 161 PART III APPLICATIONS OF ALS 11 Autonomous Learning Sensors for Chemical and Petrochemical Industries 165 11.1 Case Study
• 1: Quality of the Products in an Oil Refinery 165 11.2 Case Study
• 2: Polypropylene Manufacturing 172 11.3 Conclusions 178 12 Autonomous Learning Systems in Mobile Robotics 179 12.1 The Mobile Robot Pioneer 3DX 179 12.2 Autonomous Classifier for Landmark Recognition 180 12.3 Autonomous Leader Follower 193 12.4 Results Analysis 196 13 Autonomous Novelty Detection and Object Tracking in Video Streams 197 13.1 Problem Definition 197 13.2 Background Subtraction and KDE for Detecting Visual Novelties 198 13.3 Detecting Visual Novelties with the RDE Method 203 13.4 Object Identification in Image Frames Using RDE 204 13.5 Real-time Tracking in Video Streams Using ALS 206 13.6 Conclusions 209 14 Modelling Evolving User Behaviour with ALS 211 14.1 User Behaviour as an Evolving Phenomenon 211 14.2 Designing the User Behaviour Profile 212 14.3 Applying AutoClassify0 for Modelling Evolving User Behaviour 215 14.4 Case Studies 216 14.5 Conclusions 221 15 Epilogue 223 15.1 Conclusions 223 15.2 Open Problems 227 15.3 Future Directions 227 APPENDICES Appendix A Mathematical Foundations 231 Appendix B Pseudocode of the Basic Algorithms 235 References 245 Glossary 259 Index 263.
• (source: Nielsen Book Data)

Addresses the techniques and challenges of autonomous learning systems and illustrates the practical relevance of the approach with a wide range of applications Autonomous Learning Systems is the result of over a decade of focused research and studies in this emerging area which spans a number of well-known and well-established disciplines that include machine learning, system identification, data mining, fuzzy logic and fuzzy systems, neural networks, neuro-fuzzy systems, control theory and pattern recognition. The evolution of these systems has been both industry-driven with an increasing demand from sectors such as defence and security, aerospace and advanced process industries, bio-medicine and intelligent transportation, and research-driven - there is a strong trend of innovation of all of the above well-established research disciplines that is linked to their on-line and real-time application; their adaptability and flexibility. Providing an introduction to the key technologies, detailed technical explanations of the methodology, and an illustration of the practical relevance of the approach with a wide range of applications, this book addresses the challenges of autonomous learning systems with a systematic approach that lays the foundations for a fast growing area of research that will underpin a range of technological applications vital to both industry and society. Including a wide range of applications, illustrations and software code (MatLAB(r)) that can be used for further research it constitutes a valuable 'one-stop-shop' for academics, researchers, practicing engineers, computer specialists and defence and industry experts.
(source: Nielsen Book Data)
Autonomous Learning Systems is the result of over a decade of focused research and studies in this emerging area which spans a number of well-known and well-established disciplines that include machine learning, system identification, data mining, fuzzy logic, neural networks, neuro-fuzzy systems, control theory and pattern recognition. The evolution of these systems has been both industry-driven with an increasing demand from sectors such as defence and security, aerospace and advanced process industries, bio-medicine and intelligent transportation, as well as research-driven there is a strong trend of innovation of all of the above well-established research disciplines that is linked to their on-line and real-time application; their adaptability and flexibility. Providing an introduction to the key technologies, detailed technical explanations of the methodology, and an illustration of the practical relevance of the approach with a wide range of applications, this book addresses the challenges of autonomous learning systems with a systematic approach that lays the foundations for a fast growing area of research that will underpin a range of technological applications vital to both industry and society. Key features: * Presents the subject systematically from explaining the fundamentals to illustrating the proposed approach with numerous applications. * Covers a wide range of applications in fields including unmanned vehicles/robotics, oil refineries, chemical industry, evolving user behaviour and activity recognition. * Reviews traditional fields including clustering, classification, control, fault detection and anomaly detection, filtering and estimation through the prism of evolving and autonomously learning mechanisms. * Accompanied by a website hosting additional material, including the software toolbox and lecture notes. Autonomous Learning Systems provides a one-stop shop on the subject for academics, students, researchers and practicing engineers. It is also a valuable reference for Government agencies and software developers.
(source: Nielsen Book Data)

• Front Cover; Adaptation and Learning in Automatic Systems; Copyright Page; Contents; Foreword; Preface to the English Edition; Preface to the Russian Edition; Introduction;
• Chapter 1. Problem of Optimality; 1.1 Introduction; 1.2 Criteria of Optimality; 1.3 More about the Criteria of Optimality; 1.4 Constraints; 1.5 A Priori and Current Information; 1.6 Deterministic and Stochastic Processes; 1.7 The Ordinary and Adaptive Approaches; 1.8 On Methods for Solving Optimization Problems; 1.9 Conclusion; Comments; Bibliography;
• Chapter 2. Algorithmic Methods of Optimization; 2.1 Introduction

• 2.2 Conditions of Optimality2.3 Regular Iterative Method; 2.4 Algorithms of Optimization; 2.5 A Possible Generalization; 2.6 Various Algorithms of Optimization; 2.7 Search Algorithms of Optimization; 2.8 Constraints I; 2.9 Constraints II; 2.10 The Method of Feasible Directions; 2.11 Discussion; 2.12 Multistage Algorithms of Optimization; 2.13 Continuous Algorithms of Optimization; 2.14 Methods of Random Search; 2.15 Convergence and Stability; 2.16 The Conditions of Convergence; 2.17 On Acceleration of Convergence; 2.18 On Best Algorithms; 2.19 Examples; 2.20 Certain Problems; 2.21 Conclusion

• CommentsBibliography;
• Chapter 3. Adaptation and Learning; 3.1 Introduction; 3.2 Concepts of Learning, Self-Learning and Adaptation; 3.3 Formulation of the Problem; 3.4 Probabilistic Iterative Methods; 3.5 Algorithms of Adaptation; 3.6 Search Algorithms of Adaptation; 3.7 Constraints I; 3.8 Constraints II; 3.9 A Generalization; 3.10 Multistage Algorithms of Adaptation; 3.11 Continuous Algorithms; 3.12 Probabilistic Convergence and Stability; 3.13 Conditions of Convergence; 3.14 Stopping Rules; 3.15 Acceleration of Convergence; 3.16 Measure of Quality for the Algorithms

• 3.17 The Best Algorithms3.18 Simplified Best Algorithms; 3.19 A Special Case; 3.20 Relationship to the Least-Square Method; 3.21 Relationship to the Bayesian Approach; 3.22 Relationship to the Maximum Likelihood Method; 3.23 Discussion; 3.24 Certain Problems; 3.25 Conclusion; Comments; Bibliography;
• Chapter 4. Pattern Recognition; 4.1 Introduction; 4.2 Discussion of the Pattern Recognition Problem; 4.3 Formulation of the Problem; 4.4 General Algorithms of Training; 4.5 Convergence of the Algorithms; 4.6 Perceptrons; 4.7 Discrete Algorithms of Training; 4.8 Search Algorithms of Training

• 4.9 Continuous Algorithms of Training4.10 Comments; 4.11 More about Another General Algorithm of Training; 4.12 Special Cases; 4.13 Discussion; 4.14 Self-Learning; 4.15 The Restoration of Probability Density Functions and Moments; 4.16 Algorithms of Restoration; 4.17 Principle of Self-Learning; 4.18 Average Risk; 4.19 Variation of the Average Risk; 4.20 The Conditions for the Minimum of the Average Risk; 4.21 Algorithms of Self-Learning; 4.22 A Generalization; 4.23 Specific Algorithms; 4.24 Search Algorithms of Self-Learning; 4.25 Discussion; 4.26 Certain Problems; 4.27 Conclusion; Comments

Adaptation and learning in automatic systems.

• Introduction.- Coordination of Distributed Systems.- Coordination of Selforganising Systems.- Coordination of Pervasive Systems.- Coordination of Sociotechnical Systems.- Molecules of Knowledge: Model.- Molecules of Knowledge: Technology.-. Molecules of Knowledge: Simulation.- Molecules of Knowledge: Case Studies.- Conclusions.
• (source: Nielsen Book Data)

The book discusses the main issues of coordination in complex sociotechnical systems, covering distributed, self-organising, and pervasive systems. A chemistry-inspired model of coordination, a situated architecture and coordination language, and a cognitive model of interaction are the ingredients of the Molecules of Knowledge (MoK) model for self-organisation of knowledge presented in this book. The MoK technology is discussed, along with some case studies in the fields of collaborative systems, academic research, and citizen journalism. The target audience includes researchers and practitioners in the field of complex software systems engineering. The book is also appropriate for graduate and late undergraduate students in computer science and engineering.
(source: Nielsen Book Data)

• Complex system modelling and methodology.- A Passage to Complex Systems.- Holistic Metrics, a Trial on Interpreting Complex Systems.- Different Goals in Multiscale Simulations and How to Reach Them.- Invariant Manifolds of Complex Systems.- Application of Homotopy Perturbation Method for Ecosystems Modelling.- Swarm intelligence and neuronal learning.- Multi Objective Optimization Using Ant Colonies.- Self-Organization in an Artificial Immune Network System.- On Adapting Neural Network to Cellular Manufacturing.- Socio-environmental complex modelling and territorial intelligence.- The Evolution Process of Geographical Database within Self-Organized Topological Propagation Area.- Self-Organization Simulation over Geographical Information Systems Based on Multi-Agent Platform.- Cliff Collapse Hazards Spatio-Temporal Modelling through GIS: from Parameters Determination to Multi-scale Approach.- Structural and Dynamical Complexities of Risk and Catastrophe Systems: an Approach by System Dynamics Modelling.- Detection and Reification of Emerging Dynamical Ecosystems from Interaction Networks.- Emotion and cognition modelling.- Simulation of Emotional Processes in Decision Making.- Emotions: Theoretical Models and Clinical Implications.- Production systems and simulation.- Complex Systems Dynamics in an Economic Model with Mean Field Interactions.- Complexity of Traffic Interactions: Improving Behavioural Intelligence in Driving Simulation Scenarios.- An Integrative Simulation Model for Project Management in Chemical Process Engineering.
• (source: Nielsen Book Data)

Theconcernofthisbookistheuseofemergentcomputingandself-organiz ation modellingwithinvariousapplicationsofcomplexsystems. Wefocusouratt- tion both on the innovative concepts and implementations in order to model self-organizations, but also on the relevant applicative domains in which they can be used e?ciently. First part deals with general modelling and methodology as conceptual - proaches for complex systems description. An introductive chapter by Michel Cotsaftis entitled "A Passage to Complex Systems", treats the notion of "Complex Systems" in opposition to that of a "Complicated System". This can be, he claims, comprehended immediately from the latin roots as "C- plex" comes from "cum plexus" (tied up with) whereas "complicated" ori- natesfrom"cumpliare"(piledupwith). Thepaperisawideandrichdisser- tion with elements of history (of the technical developement of mankind) with its recents steps : mechanist, quantum and relativistic points of view. Then, the need for a "passage" is illustrated by the discussion, with tools borrowed from functional analysis, of a typical parametric di?erential system. The last and conclusive parts give tracks for the study of Complex Systems, in parti- lar one can hope to pass to quantitative study and control of complex systems even if one has to consent a "larger intelligence delegation" to them (as - nounced in the introduction) by using and developing tools already present in dissipative Physics and in Mathematical functional analysis and ?xed point theorems, for instance. This "passage" is followed by a wide bibliography of more than 90 entries. The (non hasty) reader is invited to read this deep and far reaching account before browsing through the book.
(source: Nielsen Book Data)

• Part 1. Understanding elastic leadership. Striving toward a team leader manifesto
• Matching leadership styles to team phases
• Dealing with bus factors
• Part 2. Survival mode. Dealing with survival mode
• Part 3. Learning mode. Learning to learn
• Commitment language
• Growing people
• Part 4. Self-organization mode. Using clearing meetings to advance self-organization
• Influence patterns
• The line manager manifesto
• Part 5. Notes to a software team leader.

Elastic leadership is a framework and philosophy that can help you as you manage day-to-day and long-term challenges and strive to create the elusive self-organizing team. It is about understanding that your leadership needs to change based on which phase you discover that your team is in. This book provides you with a set of values, techniques, and practices to use in your leadership role. About the Technology Your team looks to you for guidance. You have to mediate heated debates. The team is constantly putting out fires instead of doing the right things, the right way. Everyone seems to want to do things correctly, but nobody seems to be doing so. This is where leaders get stuck. It's time to get unstuck! Elastic leadership is a novel approach that helps you adapt your leadership style to the phase your team is in, so you can stay in step as things change. About the Book Elastic Leadership is a practical, experience-driven guide to team leadership. In it, you'll discover a set of values, techniques, and practices to lead your team to success. First, you'll learn what elastic leadership is and explore the phases of this results-oriented framework. Then, you'll see it in practice through stories, anecdotes, and advice provided by successful leaders in a variety of disciplines, all annotated by author and experienced team leader, Roy Osherove. What's Inside Understanding why people do what they do Effective coaching Influencing team members and managers Advice from industry leaders About the Reader This book is for anyone with a year or more of experience working on a team as a lead or team member. About the Author Roy Osherove is the DevOps process lead for the West Coast at EMC, based in California. He is also the author of The Art of Unit Testing (Manning, 2013) and Enterprise DevOps (http://enterprisedevops.org). He consults and trains teams worldwide on the gentle art of leadership, unit testing, test-driven development, and continuous-delivery automation. He frequently speaks at international conferences on these topics and others. You can find more info at these sites: ElasticLeadership.com--The blog that started this book Osherove.com--Roy's other blog Osherove Online Training (courses.osherove.com)--Roy's online training on TDD, DevOps, and, soon, elastic leadership.

• Operating mobile broadband networks --The self-organizing networks (SON) paradigm 3 multi-technology SON
• Multi-technology self-planning
• Multi-technology self-optimization
• Multi-technology self-healing
• Return on investment (ROI) for multi-technology SON.

With the current explosion in network traffic, and mounting pressure on operators business case, Self-Organizing Networks (SON) play a crucial role. They are conceived to minimize human intervention in engineering processes and at the same time improve system performance to maximize Return-on-Investment (ROI) and secure customer loyalty. Written by leading experts in the planning and optimization of Multi-Technology and Multi-Vendor wireless networks, this book describes the architecture of Multi-Technology SON for GSM, UMTS and LTE, along with the enabling technologies for SON planning, optimization and healing. This is presented mainly from a technology point of view, but also covers some critical business aspects, such as the ROI of the proposed SON functionalities and Use Cases. Key features: * Follows a truly Multi-Technology approach: covering not only LTE, but also GSM and UMTS, including architectural considerations of deploying SON in today s GSM and UMTS networks * Features detailed discussions about the relevant trade-offs in each Use Case * Includes field results of today s GSM and UMTS SON implementations in live networks * Addresses the calculation of ROI for Multi-Technology SON, contributing to a more complete and strategic view of the SON paradigm This book will appeal to network planners, optimization engineers, technical/strategy managers with operators and R&D/system engineers at infrastructure and software vendors. It will also be a useful resource for postgraduate students and researchers in automated wireless network planning and optimization.
(source: Nielsen Book Data)

• List of Figures and Tables xi Acknowledgments xv
• Chapter 1: Complexity in Ecological Systems 1 The Newtonian Paradigm in Physics 2 Dynamics and Thermodynamics 6 Emergent Properties 10 Ecosystems as Complex Adaptive Systems 13
• Chapter 2: Nonlinear Dynamics 17 The Balance of Nature?17 Population Cycles 19 Catastrophes and Breakpoints 27 Deterministic Chaos 31 Evidence of Bifurcations in Nature 34 Unpredictability and Forecasting 42 The Ecology of Universality 48 Evidence of Chaos in Nature 50 Criticisms of Chaos 58 Complex Dynamics:The Interplay between Noise and Nonlinearities 61
• Chapter 3: Spatial Self-Organization:From Pattern to Process 65 Space:The Missing Ingredient 65 Turing Instabilities 68 Coupled Map Lattice Models 84 Looking for Self-Organizing Spatial Patterns in Nature 95 Dispersal and Complex Dynamics 98 Spatial Synchrony in Population Cycles 108 When Is Space Relevant?A Trade-Off between Simplicity and Realism 117 Coevolution and Diffusion in Phenotype Space 123
• Chapter 4: Scaling and Fractals in Ecology 127 Scaling and Fractals 127 Fractal Time Series 137 Percolation 139 Nonequilibrium Phase Transitions 144 The Branching Process 146 The Contact Process:Complexity Made Simple 149 Random Walks and Levy Flights in Population Dynamics 151 Percolation and Scaling in Random Graphs 156 Ecological Multifractals 162 Self-Organized Critical Phenomena 165 Complexity from Simplicity 168
• Chapter 5: Habitat Loss and Extinction Thresholds 171 Habitat Loss and Fragmentation 171 Extinction Thresholds in Metapopulation Models 173 Extinction Thresholds in Metacommunity Models 178 Food Web Structure and Habitat Loss 186 Percolation in Spatially Explicit Landscapes 191 Extinction Thresholds in Spatially Explicit Models 195 Analytical Models of Correlated Landscapes 199 More Realistic Models of Extinction Thresholds 206
• Chapter 6: Complex Ecosystems:From Species to Networks 215 Stability and Complexity 215 N-Species Lotka-Volterra Models 218 Topological and Dynamic Constraints 223 Indirect Effects 226 Keystone Species and Evolutionary Dynamics 231 Complexity and Fragility in Food Webs 237 Community Assembly:The Importance of History 251 Scaling in Ecosystems:A Stochastic Quasi-Neutral Model 254
• Chapter 7: Complexity in Macroevolution 263 Extinction and Diversification 263 Internal and External Factors 264 Scaling in the Fossil Recor 270 Competition and the Fossil Recor 276 Red Queen Dynamics 279 Evolution on Fitness Landscapes 282 Extinctions and Coherent Noise 292 NetworkModels of Macroevolution 295 Ecology as It Would Be: Artificial Life 304 Recovery after Mass Extinction 308 Implications for Current Ecologies 313
• Appendix 1.Lyapunov Exponents for ID Maps 317 Appendix 2.Renormalization Group Analysis 319 Appendix 3.Stochastic Multispecies Model 321
• References 325 Index 359.
• (source: Nielsen Book Data)

Can physics be an appropriate framework for the understanding of ecological science? Most ecologists would probably agree that there is little relation between the complexity of natural ecosystems and the simplicity of any example derived from Newtonian physics. Though ecologists have long been interested in concepts originally developed by statistical physicists and later applied to explain everything from why stock markets crash to why rivers develop particular branching patterns, applying such concepts to ecosystems has remained a challenge. "Self-Organization in Complex Ecosystems" is the first book to clearly synthesize what we have learned about the usefulness of tools from statistical physics in ecology. Ricard Sole and Jordi Bascompte provide a comprehensive introduction to complex systems theory, and ask: do universal laws shape the structure of ecosystems, at least at some scales? They offer the most compelling array of theoretical evidence to date of the potential of nonlinear ecological interactions to generate nonrandom, self-organized patterns at all levels. Tackling classic ecological questions - from population dynamics to biodiversity to macroevolution - the book's novel presentation of theories and data shows the power of statistical physics and complexity in ecology. "Self-Organization in Complex Ecosystems" will be a staple resource for years to come for ecologists interested in complex systems theory as well as mathematicians and physicists interested in ecology.
(source: Nielsen Book Data)

• An Introduction.- Advanced Topics.
• (source: Nielsen Book Data)

This book is an often-requested reprint of two classic texts by H. Haken: "Synergetics. An Introduction" and "Advanced Synergetics". Synergetics, an interdisciplinary research program initiated by H. Haken in 1969, deals with the systematic and methodological approach to the rapidly growing field of complexity. Going well beyond qualitative analogies between complex systems in fields as diverse as physics, chemistry, biology, sociology and economics, Synergetics uses tools from theoretical physics and mathematics to construct an unifying framework within which quantitative descriptions of complex, self-organizing systems can be made.This may well explain the timelessness of H. Haken's original texts on this topic, which are now recognized as landmarks in the field of complex systems. They provide both the beginning graduate student and the seasoned researcher with solid knowledge of the basic concepts and mathematical tools. Moreover, they admirably convey the spirit of the pioneering work by the founder of Synergetics through the essential applications contained herein that have lost nothing of their paradigmatic character since they were conceived.
(source: Nielsen Book Data)

• Intro
• Foreword
• Preface
• Organization of the Chapters
• Acknowledgments
• Contents
• Contributors
• Part I Methodology
• 1 SOMA
• Self-organizing Migrating Algorithm
• Abstract
• 1 Introduction
• 2 Historical Background and Algorithm Classification
• 2.1 SOMA in the Context of Selected Evolutionary Algorithms
• 3 SOMA Applicability
• 4 SOMA Principles and Control Parameters
• 5 SOMA Strategies
• 5.1 SOMA Parameters
• 5.2 Standard Evolutionary Operations in SOMA
• 5.2.1 Population
• 5.2.2 Mutations
• 5.2.3 Crossover
• 5.2.4 Constraint Handling

• 5.2.5 Boundary Constraints
• 5.2.6 Constraint Functions
• 5.2.7 Handling of Integer and Discrete Variables
• 6 Parameter Dependence
• 7 SOMA and Cost Function Evaluations
• 8 Selected SOMA Applications
• 9 SOMA in Computer Games
• 10 SOMA and Interdisciplinary Research
• 11 Conclusion
• Acknowledgments
• References
• 2 DSOMA
• Discrete Self Organising Migrating Algorithm
• Abstract
• 1 Introduction
• 2 Discrete Self-organising Migrating Algorithm
• 3 Initialisation
• 4 Creating Jump Sequences
• 5 Constructing Trial Individuals
• 6 Repairing Trial Individuals

• 7 Population Update
• 8 Iteration
• 9 Migrations
• 10 2 Opt Local Search
• 11 Conclusion
• Acknowledgments
• References
• Part II Implementation
• 3 SOMA and Strange Dynamics
• Abstract
• 1 Introduction
• 2 SOMA and Chaos
• 2.1 Chaos Synthesis
• 2.2 Chaos Control
• 2.3 Chaos Identification
• 2.4 SOMA Powered by Pseudorandom, Chaos and Deterministic Dynamics
• 3 SOMA and Fractal Geometry
• 4 SOMA Dynamics as a Complex Networks
• 5 Conclusion
• Acknowledgment
• References
• 4 Multi-objective Self-organizing Migrating Algorithm
• Abstract

• 1 Introduction to Multi-objective Optimization
• 2 MOSOMA
• 2.1 Controlling Parameters
• 2.2 Migration of Agents
• 2.3 Final Non-dominated Set Choice
• 3 Appendix I
• Evaluation Metrics
• 4 Appendix II
• Benchmark Problems
• Acknowledgements
• References
• 5 Multi-objective Design of EM Components
• Abstract
• 1 Design of EM Components
• 1.1 Yagi-Uda Antenna Design
• 1.2 Dielectric Layered Filter Design
• 1.3 Adaptive Beamforming in Time Domain
• Acknowledgements
• References
• 6 Utilization of Parallel Computing for Discrete Self-organizing Migration Algorithm
• Abstract

• 1 Introduction
• 2 Levels of Parallelization
• 3 Hardware and Software Options for Parallelization
• 3.1 OpenMP
• 3.2 Message Passing Interface
• 3.2.1 Brief Introduction into Kaira
• 3.3 GPU Computing with CUDA
• 4 Parallelization of DSOMA
• 4.1 OpenMP Implementation of DSOMA
• 4.2 Distributed Island Model Implementation of DSOMA
• 4.3 GPU Implementation
• 4.3.1 Data Storage, Transfers and Alignment
• 4.3.2 Data Level Prallelism
• 4.3.3 Single Thread Computation
• 4.3.4 Block/Warp Computation
• 5 Experiments
• 5.1 OpenMP Experiments
• 5.2 CUDA Experiments

This book brings together the current state of-the-art research in Self Organizing Migrating Algorithm (SOMA) as a novel population-based evolutionary algorithm, modeled on the predator-prey relationship, by its leading practitioners. As the first ever book on SOMA, this book is geared towards graduate students, academics and researchers, who are looking for a good optimization algorithm for their applications. This book presents the methodology of SOMA, covering both the real and discrete domains, and its various implementations in different research areas. The easy-to-follow and implement methodology used in the book will make it easier for a reader to implement, modify and utilize SOMA.

• The concept of self-assembling
• Starch and derivatives as pharmaceutical excipients
• Chitosan and derivatives as biomaterials and as pharmaceutical excipients
• Polyelectrolyte complexes as excipients for oral administration
• Natural semi-synthetic and synthetic materials
• Protein-protein associative interactions and their involvement in bioformulations
• Self-assembling materials, implants and xenografts
• Conclusions and perspectives.
• (source: Nielsen Book Data)

In complex macromolecules, minor modifications can generate major changes, due to self-assembling capacities of macromolecular or supramolecular networks. Controlled Drug Delivery highlights how the multifunctionality of several materials can be achieved and valorized for pharmaceutical and biopharmaceutical applications. Topics covered in this comprehensive book include: the concept of self-assembling; starch and derivatives as pharmaceutical excipients; and chitosan and derivatives as biomaterials and as pharmaceutical excipients. Later chapters discuss polyelectrolyte complexes as excipients for oral administration; and natural semi-synthetic and synthetic materials. Closing chapters cover protein-protein associative interactions and their involvement in bioformulations; self-assembling materials, implants and xenografts; and provide conclusions and perspectives. * Offers novel perspectives of a new concept: how minor alterations can induce major self-stabilization by cumulative forces exerted at short and long distances* Gives guidance on how to approach modifications of biopolymers for drug delivery systems and materials for implants* Describes structure-properties relationships in proposed excipients, drug delivery systems and biomedical materials.
(source: Nielsen Book Data)

• The frontiers and challenges of biodynamics research Jan Walleczek
• Part I. Nonlinear Dynamics in Biology and Response to Stimuli: 1. External signals and internal oscillation dynamics - principal aspects and response of stimulated rhythmic processes Friedemann Kaiser
• 2. Nonlinear dynamics in biochemical and biophysical systems: from enzyme kinetics to epilepsy Raima Larter, Robert Worth and Brent Speelman
• 3. Fractal mechanisms in neural control: human heartbeat and gait dynamics in health and disease Chung-Kang Peng, Jeffrey M. Hausdorff and Ary L. Goldberger
• 4. Self-organising dynamics in human coordination and perception Mingzhou Ding, Yanqing Chen, J. A. Scott Kelso and Betty Tuller
• 5. Signal processing in biochemical reaction networks Adam P. Arkin
• Part II. Nonlinear Sensitivity of Biological Systems to Electromagnetic Stimuli: 6. Electrical signal detection and noise in systems with long-range coherence Paul C. Gailey
• 7. Oscillatory signals in migrating neutrophils: effects of time-varying chemical and electrical fields Howard R. Petty
• 8. Enzyme kinetics and nonlinear biochemical amplification in response to static and oscillating magnetic fields Jan Walleczek and Clemens F. Eichwald
• 9. Magnetic field sensitivity in the hippocampus Stefan Engstroem, Suzanne Bawin and W. Ross Adey
• Part III. Stochastic Noise-Induced Dynamics and Transport in Biological Systems: 10. Stochastic resonance: looking forward Frank Moss
• 11. Stochastic resonance and small-amplitude signal transduction in voltage-gated ion channels Sergey M. Bezrukov and Igor Vodyanoy
• 12. Ratchets, rectifiers and demons: the constructive role of noise in free energy and signal transduction R. Dean Astumian
• 13. Cellular transduction of periodic and stochastic energy signals by electroconformational coupling Tian Y. Tsong
• Part IV. Nonlinear Control of Biological and Other Excitable Systems: 14. Controlling chaos in dynamical systems Kenneth Showalter
• 15. Electromagnetic fields and biological tissues: from nonlinear response to chaos control William L. Ditto and Mark L. Spano
• 16. Epilepsy: multistability in a dynamic disease John G. Milton
• 17. Control and perturbation of wave propagation in excitable systems Oliver Steinbock and Stefan C. Muller
• 18. Changing paradigms in biomedicine: implications for future research and clinical applications Jan Walleczek
• Index.
• (source: Nielsen Book Data)

The growing impact of nonlinear science on biology and medicine is fundamentally changing our view of living organisms and disease processes. This book introduces the application to biomedicine of a broad range of interdisciplinary concepts from nonlinear dynamics, such as self-organization, complexity, coherence, stochastic resonance, fractals and chaos. It comprises 18 chapters written by leading figures in the field and covers experimental and theoretical research, as well as the emerging technological possibilities such as nonlinear control techniques for treating pathological biodynamics, including heart arrhythmias and epilepsy. This book will attract the interest of professionals and students from a wide range of disciplines, including physicists, chemists, biologists, sensory physiologists and medical researchers such as cardiologists, neurologists and biomedical engineers.
(source: Nielsen Book Data)

• Behavioural modelling, planning, and learning
• synthetic autonomy
• dynamics of distributed computation
• self-organized autonomy in multi-agent systems
• autonomy-oriented computation
• dynamics and complexity of autonomy-oriented computation.
• (source: Nielsen Book Data)

An autonomous agent is a computational system that acquires sensory data from its environment and decides by itself how to relate the external stimulus to its behaviors in order to attain certain goals. Responding to different stimuli received from its task environment, the agent may select and exhibit different behavioral patterns. The behavioral patterns may be carefully predefined or dynamically acquired by the agent based on some learning and adaptation mechanism(s). In order to achieve structural flexibility, reliability through redundancy, adaptability, and reconfigurability in real-world tasks, some researchers have started to address the issue of multiagent cooperation. Broadly speaking, the power of autonomous agents lies in their ability to deal with unpredictable, dynamically changing environments. Agent-based systems are becoming one of the most important computer technologies, holding out many promises for solving real-world problems.The aims of this book are to provide a guided tour to the pioneering work and the major technical issues in agent research, and to give an in-depth discussion on the computational mechanisms for behavioral engineering in autonomous agents. Through a systematic examination, the book attempts to provide the general design principles for building autonomous agents and the analytical tools for modeling the emerged behavioral properties of a multiagent system.
(source: Nielsen Book Data)