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• Introduction.- Radon Transform based Automatic Posture Recognition in Ballet Dance.- Fuzzy Image Matching Based Posture Recognition in Ballet Dance.- Gesture Driven Fuzzy Interface System For Car Racing Game.- Type-2 Fuzzy Classifier based Pathological Disorder Recognition.- Probabilistic Neural Network based Dance Gesture Recognition.- Differential Evolution based Dance Composition.- EEG-Gesture based Artificial Limb Movement for Rehabilitative Applications.- Conclusions and Future Directions.- Index.
• (source: Nielsen Book Data)

This book presents a thorough analysis of gestural data extracted from raw images and/or range data with an aim to recognize the gestures conveyed by the data. It covers image morphological analysis, type-2 fuzzy logic, neural networks and evolutionary computation for classification of gestural data. The application areas include the recognition of primitive postures in ballet/classical Indian dances, detection of pathological disorders from gestural data of elderly people, controlling motion of cars in gesture-driven gaming and gesture-commanded robot control for people with neuro-motor disability. The book is unique in terms of its content, originality and lucid writing style. Primarily intended for graduate students and researchers in the field of electrical/computer engineering, the book will prove equally useful to computer hobbyists and professionals engaged in building firmware for human-computer interfaces. A prerequisite of high school level mathematics is sufficient to understand most of the chapters in the book. A basic background in image processing, although not mandatory, would be an added advantage for certain sections.
(source: Nielsen Book Data)

• Preface; Acknowledgements; Contents; About the Authors; 1 An Introduction to Time-Series Prediction; Abstract; 1.1 Defining Time-Series; 1.2 Importance of Time-Series Prediction; 1.3 Hindrances in Economic Time-Series Prediction; 1.4 Machine Learning Approach to Time-Series Prediction; 1.5 Scope of Machine Learning in Time-Series Prediction; 1.6 Sources of Uncertainty in a Time-Series; 1.7 Scope of Uncertainty Management by Fuzzy Sets; 1.8 Fuzzy Time-Series; 1.8.1 Partitioning of Fuzzy Time-Series; 1.8.2 Fuzzification of a Time-Series; 1.9 Time-Series Prediction Using Fuzzy Reasoning

• 1.10 Single and Multi-Factored Time-Series Prediction1.11 Scope of the Book; 1.12 Summary; References; 2 Self-adaptive Interval Type-2 Fuzzy Set Induced Stock Index Prediction; Abstract; 2.1 Introduction; 2.2 Preliminaries; 2.3 Proposed Approach; 2.3.1 Training Phase; 2.3.1.1 Partitioning of Main Factor Close Prices into p Intervals of Equal Length; 2.3.1.2 Construction of IT2 or Type-1 Fuzzy Sets as Appropriate for Each Interval of Close Price; 2.3.1.3 Fuzzy Prediction Rule (FPR) Construction for Consecutive {\varvec c(t) } s

• 2.3.1.4 Grouping of IT2/T1 Fuzzy Implications for Individual Main Factor Variation {\varvec V_{M}^{d} } (t)2.3.1.5 Computing Composite Secondary Variation Series (CSVS) and Its Partitioning; 2.3.1.6 Determining Secondary to Main Factor Variation Mapping; 2.3.2 Prediction Phase; 2.3.3 Prediction with Self-adaptive IT2/T1 MFs; 2.4 Experiments; 2.4.1 Experimental Platform; 2.4.2 Experimental Modality and Results; 2.4.2.1 Policies Adopted; 2.4.2.2 MF Selection; 2.4.2.3 Adaptation Cycle; 2.4.2.4 Varying d; 2.5 Performance Analysis; 2.6 Conclusion; 2.7 Exercises; Appendix 2.1

• Appendix 2
• .2: Source Codes of the ProgramsReferences; 3 Handling Main and Secondary Factors in the Antecedent for Type-2 Fuzzy Stock Prediction; Abstract; 3.1 Introduction; 3.2 Preliminaries; 3.3 Proposed Approach; 3.3.1 Method-I: Prediction Using Classical IT2FS; 3.3.2 Method-II: Secondary Factor Induced IT2 Approach; 3.3.3 Method-III: Prediction in Absence of Sufficient Data Points; 3.3.4 Method-IV: Adaptation of Membership Function in Method III to Handle Dynamic Behaviour of Time-Series [47-52]; 3.4 Experiments; 3.4.1 Experimental Platform; 3.4.2 Experimental Modality and Results

• 3.5 ConclusionAppendix 3
• .1: Differential Evolution Algorithm [36, 48-50]; References; 4 Learning Structures in an Economic Time-Series for Forecasting Applications; Abstract; 4.1 Introduction; 4.2 Related Work; 4.3 DBSCAN Clustering-An Overview; 4.4 Slope-Sensitive Natural Segmentation; 4.4.1 Definitions; 4.4.2 The SSNS Algorithm; 4.5 Multi-level Clustering of Segmented Time-Blocks; 4.5.1 Pre-processing of Temporal Segments; 4.5.2 Principles of Multi-level DBSCAN Clustering; 4.5.3 The Multi-level DBSCAN Clustering Algorithm; 4.6 Knowledge Representation Using Dynamic Stochastic Automaton

• Preface xix
• Acknowledgments xxvii
• Contributors xxix
• 1 Introduction to Emotion Recognition 1 Amit Konar, Anisha Halder, and Aruna Chakraborty
• 1.1 Basics of Pattern Recognition, 1
• 1.2 Emotion Detection as a Pattern Recognition Problem, 2
• 1.3 Feature Extraction, 3
• 1.4 Feature Reduction Techniques, 15
• 1.5 Emotion Classification, 17
• 1.6 Multimodal Emotion Recognition, 24
• 1.7 Stimulus Generation for Emotion Arousal, 24
• 1.8 Validation Techniques, 26
• 1.9 Summary, 27
• References, 28
• Author Biographies, 44
• 2 Exploiting Dynamic Dependencies Among Action Units for Spontaneous Facial Action Recognition 47 Yan Tong and Qiang Ji
• 2.1 Introduction, 48
• 2.2 Related Work, 49
• 2.3 Modeling the Semantic and Dynamic Relationships Among AUs With a DBN, 50
• 2.4 Experimental Results, 60
• 2.5 Conclusion, 64
• References, 64
• Author Biographies, 66
• 3 Facial Expressions: A Cross-Cultural Study 69 Chandrani Saha, Washef Ahmed, Soma Mitra, Debasis Mazumdar, and Sushmita Mitra
• 3.1 Introduction, 69
• 3.2 Extraction of Facial Regions and Ekman s Action Units, 71
• 3.3 Cultural Variation in Occurrence of Different AUs, 76
• 3.4 Classification Performance Considering Cultural Variability, 79
• 3.5 Conclusion, 84
• References, 84
• Author Biographies, 86
• 4 A Subject-Dependent Facial Expression Recognition System 89 Chuan-Yu Chang and Yan-Chiang Huang
• 4.1 Introduction, 89
• 4.2 Proposed Method, 91
• 4.3 Experiment Result, 103
• 4.4 Conclusion, 109
• Acknowledgment, 110
• References, 110
• Author Biographies, 112
• 5 Facial Expression Recognition Using Independent Component Features and Hidden Markov Model 113 Md. Zia Uddin and Tae-Seong Kim
• 5.1 Introduction, 114
• 5.2 Methodology, 115
• 5.3 Experimental Results, 123
• 5.4 Conclusion, 125
• Acknowledgments, 125
• References, 126
• Author Biographies, 127
• 6 Feature Selection for Facial Expression Based on Rough Set Theory 129 Yong Yang and Guoyin Wang
• 6.1 Introduction, 129
• 6.2 Feature Selection for Emotion Recognition Based on Rough Set Theory, 131
• 6.3 Experiment Results and Discussion, 137
• 6.4 Conclusion, 143
• Acknowledgments, 143
• References, 143
• Author Biographies, 145
• 7 Emotion Recognition from Facial Expressions Using Type-2 Fuzzy Sets 147 Anisha Halder, Amit Konar, Aruna Chakraborty, and Atulya K. Nagar
• 7.1 Introduction, 148
• 7.2 Preliminaries on Type-2 Fuzzy Sets, 150
• 7.3 Uncertainty Management in Fuzzy-Space for Emotion Recognition, 152
• 7.4 Fuzzy Type-2 Membership Evaluation, 157
• 7.5 Experimental Details, 161
• 7.6 Performance Analysis, 167
• 7.7 Conclusion, 175
• References, 176
• Author Biographies, 180
• 8 Emotion Recognition from Non-frontal Facial Images 183 Wenming Zheng, Hao Tang, and Thomas S. Huang
• 8.1 Introduction, 184
• 8.2 A Brief Review of Automatic Emotional Expression Recognition, 187
• 8.3 Databases for Non-frontal Facial Emotion Recognition, 191
• 8.4 Recent Advances of Emotion Recognition from Non-Frontal Facial Images, 196
• 8.5 Discussions and Conclusions, 205
• Acknowledgments, 206
• References, 206
• Author Biographies, 211
• 9 Maximum a Posteriori Based Fusion Method for Speech Emotion Recognition 215 Ling Cen, Zhu Liang Yu, and Wee Ser
• 9.1 Introduction, 216
• 9.2 Acoustic Feature Extraction for Emotion Recognition, 219
• 9.3 Proposed Map-Based Fusion Method, 223
• 9.4 Experiment, 229
• 9.5 Conclusion, 232
• References, 232
• Author Biographies, 234
• 10 Emotion Recognition in Naturalistic Speech and Language A Survey 237 Felix Weninger, Martin Wollmer, and Bjoern Schuller
• 10.1 Introduction, 238
• 10.2 Tasks and Applications, 239
• 10.3 Implementation and Evaluation, 244
• 10.4 Challenges, 253
• 10.5 Conclusion and Outlook, 257
• Acknowledgment, 259
• References, 259
• Author Biographies, 267
• 11 EEG-Based Emotion Recognition Using Advanced Signal Processing Techniques 269 Panagiotis C. Petrantonakis and Leontios J. Hadjileontiadis
• 11.1 Introduction, 270
• 11.2 Brain Activity and Emotions, 271
• 11.3 EEG-ER Systems: An Overview, 272
• 11.4 Emotion Elicitation, 273
• 11.5 Advanced Signal Processing in EEG-ER, 275
• 11.6 Concluding Remarks and Future Directions, 287
• References, 289
• Author Biographies, 292
• 12 Frequency Band Localization on Multiple Physiological Signals for Human Emotion Classification Using DWT 295 M. Murugappan
• 12.1 Introduction, 296
• 12.2 Related Work, 297
• 12.3 Research Methodology, 299
• 12.4 Experimental Results and Discussions, 306
• 12.5 Conclusion, 310
• 12.6 Future Work, 310
• Acknowledgments, 310
• References, 310
• Author Biography, 312
• 13 Toward Affective Brain Computer Interface: Fundamentals and Analysis of EEG-Based Emotion Classification 315 Yuan-Pin Lin, Tzyy-Ping Jung, Yijun Wang, and Julie Onton
• 13.1 Introduction, 316
• 13.2 Materials and Methods, 323
• 13.3 Results and Discussion, 327
• 13.4 Conclusion, 332
• 13.5 Issues and Challenges Toward ABCIs, 332
• Acknowledgments, 336
• References, 336
• Author Biographies, 340
• 14 Bodily Expression for Automatic Affect Recognition 343 Hatice Gunes, Caifeng Shan, Shizhi Chen, and YingLi Tian
• 14.1 Introduction, 344
• 14.2 Background and Related Work, 345
• 14.3 Creating a Database of Facial and Bodily Expressions: The FABO Database, 353
• 14.4 Automatic Recognition of Affect from Bodily Expressions, 356
• 14.5 Automatic Recognition of Bodily Expression Temporal Dynamics, 361
• 14.6 Discussion and Outlook, 367
• 14.7 Conclusions, 369
• Acknowledgments, 370
• References, 370
• Author Biographies, 375
• 15 Building a Robust System for Multimodal Emotion Recognition 379 Johannes Wagner, Florian Lingenfelser, and Elisabeth Andre
• 15.1 Introduction, 380
• 15.2 Related Work, 381
• 15.3 The Callas Expressivity Corpus, 382
• 15.4 Methodology, 386
• 15.5 Multisensor Data Fusion, 390
• 15.6 Experiments, 395
• 15.7 Online Recognition System, 399
• 15.8 Conclusion, 403
• Acknowledgment, 404
• References, 404
• Author Biographies, 410
• 16 Semantic Audiovisual Data Fusion for Automatic Emotion Recognition 411 Dragos Datcu and Leon J. M. Rothkrantz
• 16.1 Introduction, 412
• 16.2 Related Work, 413
• 16.3 Data Set Preparation, 416
• 16.4 Architecture, 418
• 16.5 Results, 431
• 16.6 Conclusion, 432
• References, 432
• Author Biographies, 434
• 17 A Multilevel Fusion Approach for Audiovisual Emotion Recognition 437 Girija Chetty, Michael Wagner, and Roland Goecke
• 17.1 Introduction, 437
• 17.2 Motivation and Background, 438
• 17.3 Facial Expression Quantification, 440
• 17.4 Experiment Design, 444
• 17.5 Experimental Results and Discussion, 450
• 17.6 Conclusion, 456
• References, 456
• Author Biographies, 459
• 18 From a Discrete Perspective of Emotions to Continuous, Dynamic, and Multimodal Affect Sensing 461 Isabelle Hupont, Sergio Ballano, Eva Cerezo, and Sandra Baldassarri
• 18.1 Introduction, 462
• 18.2 A Novel Method for Discrete Emotional Classification of Facial Images, 465
• 18.3 A 2D Emotional Space for Continuous and Dynamic Facial Affect Sensing, 469
• 18.4 Expansion to Multimodal Affect Sensing, 474
• 18.5 Building Tools That Care, 479
• 18.6 Concluding Remarks and Future Work, 486
• Acknowledgments, 488
• References, 488
• Author Biographies, 491
• 19 Audiovisual Emotion Recognition Using Semi-Coupled Hidden Markov Model with State-Based Alignment Strategy 493 Chung-Hsien Wu, Jen-Chun Lin, and Wen-Li Wei
• 19.1 Introduction, 494
• 19.2 Feature Extraction, 495
• 19.3 Semi-Coupled Hidden Markov Model, 500
• 19.4 Experiments, 504
• 19.5 Conclusion, 508
• References, 509
• Author Biographies, 512
• 20 Emotion Recognition in Car Industry 515 Christos D. Katsis, George Rigas, Yorgos Goletsis, and Dimitrios I. Fotiadis
• 20.1 Introduction, 516
• 20.2 An Overview of Application for the Car Industry, 517
• 20.3 Modality-Based Categorization, 517
• 20.4 Emotion-Based Categorization, 520
• 20.5 Two Exemplar Cases, 523
• 20.6 Open Issues and Future Steps, 536
• 20.7 Conclusion, 537
• References, 537
• Author Biographies, 543
• Index 545.
• (source: Nielsen Book Data)

A timely book containing foundations and current research directions on emotion recognition by facial expression, voice, gesture and biopotential signals This book provides a comprehensive examination of the research methodology of different modalities of emotion recognition. Key topics of discussion include facial expression, voice and biopotential signal-based emotion recognition. Special emphasis is given to feature selection, feature reduction, classifier design and multi-modal fusion to improve performance of emotion-classifiers. Written by several experts, the book includes several tools and techniques, including dynamic Bayesian networks, neural nets, hidden Markov model, rough sets, type-2 fuzzy sets, support vector machines and their applications in emotion recognition by different modalities. The book ends with a discussion on emotion recognition in automotive fields to determine stress and anger of the drivers, responsible for degradation of their performance and driving-ability. There is an increasing demand of emotion recognition in diverse fields, including psycho-therapy, bio-medicine and security in government, public and private agencies. The importance of emotion recognition has been given priority by industries including Hewlett Packard in the design and development of the next generation human-computer interface (HCI) systems. Emotion Recognition: A Pattern Analysis Approach would be of great interest to researchers, graduate students and practitioners, as the book Offers both foundations and advances on emotion recognition in a single volume Provides a thorough and insightful introduction to the subject by utilizing computational tools of diverse domains Inspires young researchers to prepare themselves for their own research Demonstrates direction of future research through new technologies, such as Microsoft Kinect, EEG systems etc.
(source: Nielsen Book Data)

• The Psychological Basis of Cognitive Modeling.- Parallel and Distributed Logic Programming.- Distributed Reasoning by Fuzzy Petri Nets: A Review.- Belief Propagation and Belief Revision Models in Fuzzy Petri Nets.- Building Expert Systems Using Fuzzy Petri Nets.- Distributed Learning Using Fuzzy Cognitive Maps.- Unsupervised Learning by Fuzzy Petri Nets.- Supervised Learning by a Fuzzy Petri Net.- Distributed Modeling of Abduction, Reciprocity, and Duality by Fuzzy Petri Nets.- Human Mood Detection and Control: A Cybernetic Approach.- Distributed Planning and Multi-agent Coordination of Robots.
• (source: Nielsen Book Data)

What we profoundly witness these days is a growing number of human-centric systems and a genuine interest in a comprehensive understanding of their underlying paradigms and the development of solid and efficient design practices. We are indeed in the midst of the next information revolution, which very likely brings us into a completely new world of ubiquitous and invisible computing, Ambient Intelligent (AMI), and wearable hardware. This requires a totally new way of thinking in which cognitive aspects of design, cognitive system engineering and distributed approach play a pivotal role. This book fully addresses these timely needs by filling a gap between the two well-established disciplines of cognitive sciences and cognitive systems engineering. As we put succinctly in the preface, with the psychological perspective of human cognition in mind, "the book explores the computational models of reasoning, learning, planning and multi-agent coordination and control of the human moods". This is an excellent, up to the point description of the book. The treatise is focused on the underlying fundamentals, spans across a vast territory embracing logic perspectives of human cognition, distributed models, parallel computing, expert systems, and intelligent robotics.
(source: Nielsen Book Data)

• The Psychological Basis of Cognitive Modeling.- Parallel and Distributed Logic Programming.- Distributed Reasoning by Fuzzy Petri Nets: A Review.- Belief Propagation and Belief Revision Models in Fuzzy Petri Nets.- Building Expert Systems Using Fuzzy Petri Nets.- Distributed Learning Using Fuzzy Cognitive Maps.- Unsupervised Learning by Fuzzy Petri Nets.- Supervised Learning by a Fuzzy Petri Net.- Distributed Modeling of Abduction, Reciprocity, and Duality by Fuzzy Petri Nets.- Human Mood Detection and Control: A Cybernetic Approach.- Distributed Planning and Multi-agent Coordination of Robots.
• (source: Nielsen Book Data)

What we profoundly witness these days is a growing number of human-centric systems and a genuine interest in a comprehensive understanding of their underlying paradigms and the development of solid and efficient design practices. We are indeed in the midst of the next information revolution, which very likely brings us into a completely new world of ubiquitous and invisible computing, Ambient Intelligent (AMI), and wearable hardware. This requires a totally new way of thinking in which cognitive aspects of design, cognitive system engineering and distributed approach play a pivotal role. This book fully addresses these timely needs by filling a gap between the two well-established disciplines of cognitive sciences and cognitive systems engineering. As we put succinctly in the preface, with the psychological perspective of human cognition in mind, "the book explores the computational models of reasoning, learning, planning and multi-agent coordination and control of the human moods". This is an excellent, up to the point description of the book. The treatise is focused on the underlying fundamentals, spans across a vast territory embracing logic perspectives of human cognition, distributed models, parallel computing, expert systems, and intelligent robotics.
(source: Nielsen Book Data)

• An Introduction to Computational Intelligence
• Fuzzy Sets and Relations
• Fuzzy Logic and Approximate Reasoning
• Fuzzy Logic in Process Control
• Fuzzy Pattern Recognition
• Fuzzy Databases and Possibilistic Reasoning
• to Machine Learning Using Neural Nets
• Supervised Neural Learning Algorithms
• Unsupervised Neural Learning Algorithms
• Competitive Learning Using Neural Nets
• Neuro-dynamic Programming by Reinforcement Learning
• Evolutionary Computing Algorithms
• Belief Calculus and Probabilistic Reasoning
• Reasoning in Expert Systems Using Fuzzy Petri Nets
• Fuzzy Models for Face Matching and Mood Detection
• Behavioral Synergism of Soft Computing Tools
• Object Recognition from Gray Images Using Fuzzy ADALINE Neurons
• Distributed Machine Learning Using Fuzzy Cognitive Maps
• Machine Learning Using Fuzzy Petri Nets
• Computational Intelligence in Tele-Communication Networks
• Computational Intelligence in Mobile Robotics
• Emerging Areas of Computational Intelligence
• Research Problems for Graduate Thesis and Pre-Ph D Preparatory Courses.

"The book Computational Intelligence: Principles, Techniques and Applications presents both theories and applications of Computational Intelligence in a clear, precise and highly comprehensive style. The textbook addresses the fundamental aspects of Fuzzy Sets and Logic, Neural Networks, Evolutionary Computing and Belief Networks. The application areas include Fuzzy Databases, Fuzzy Control, Image Understanding, Expert Systems, Object Recognition, Criminal Investigation, Telecommunication Networks and Intelligent Robots. The book contains many numerical examples and homework problems with sufficient hints so that the students can solve them on their own. Emerging areas of Computational Intelligence such as artificial life, particle swarm optimization, artificial immune systems, fuzzy chaos theory, rough sets and granular computing have also been addressed with examples in this book. The book ends with a discussion on a number of open-ended research problems in Computational Intelligence. Graduate students interested to pursue their research in this subject will greatly be benefited with these problems. A CD ROM containing the simulations is supplied with the book to enable the interested readers to develop their own application programs with the supplied C/C++ toolbox."--Jacket

• INTRODUCTION TO AI AND SOFT COMPUTING Evolution of Computing Defining AI General Problem Solving Approaches in AI The Disciplines of AI A Brief History of AI Characteristic Requirement for the Realization of Intelligent Systems Programming Languages for AI Architecture for AI Machines Objective and Scope of the Book Summary THE PSYCHOLOGICAL PERSPECTIVE OF COGNITION Introduction The Cognitive Perspective of Pattern Recognition Cognitive Models of Memory Mental Imagery Understanding a Problem A Cybernetic View to Cognition Scope of Realization of Cognition in AI Summary PRODUCTION SYSTEMS Introduction Production Rules The Working Memory The Control Unit / Interpreter Conflict Resolution Strategies An Alternative Approach for Conflict Resolution An Illustrative Production System The RETE Match Algorithm Types of Production Systems Forward versus Backward Production Systems General Merits of a Production System Knowledge Base Optimization in a Production System Conclusions PROBLEM SOLVING BY INTELLIGENT SEARCH Introduction General Problem Solving Approaches Heuristic Search Adversary Search Conclusions THE LOGIC OF PROPOSITIONS AND PREDICATES Introduction Formal Definitions Tautologies in Propositional Logic Theorem Proving by Propositional Logic Resolution in Propositional Logic Soundness and Completeness of Propositional Logic Predicate Logic Writing a Sentence into Clause Forms Unification of Predicates Robinson's Inference Rule Different Types of Resolution Semi-Decidability Soundness and Completeness of Predicate Logic Conclusions PRINCIPLES OF LOGIC PROGRAMMING Introduction to PROLOG Programming Logic Programs - A Formal Definition A Scene Interpretation Program Illustrating Backtracking by flow of Satisfaction Diagrams The SLD Resolution Controlling Backtracking by CUT The NOT Predicate Negation as a Failure in Extended Logic Programs Fixed Points in Non-Horn Clause Based Programs Constraint Logic Programming Conclusions DEFAULT AND NON-MONOTONIC REASONING Introduction Monotonic versus Non-Monotonic Logic Non-Monotonic Resoning Using NML-I Fixed Points in Non-Monotonic Reasoning Non-Monotonic Resoning Using NML-II Truth Maintenance System Default Reasoning The Closed World Assumption Circumscription Auto-Epistemic Logic Conclusions STRUCTURED APPROACH TO KNOWLEDGE REPRESENTATION Introduction Semantic Nets Inheritance in Semantic Nets Manipulating Monotonic and Default Inheritance in Semantic Nets Defeasible Reasoning in Semantic Nets Frames Inheritance in Tangled Frames Petri nets Conceptual Dependency Scripts Conclusions DEALING WITH IMPRECISION AND UNCERTAINTY Introduction Probabilistic Reasoning Certainty Factor Based Reasoning Fuzzy Reasoning Comparison of the Proposed Models STRUCTURED APPROACH TO FUZZY REASONING Introduction Structural Model of Fuzzy FPN and Reachability Analysis Behavioral Model of FPN and Stability Analysis Forward Reasoning in FPN Backward Reasoning in FPN Bi-directional IFF Type Reasoning and Reciprocity Fuzzy Modus Tollens and Duality Non-Monotonic Reasoning in an FPN Conclusions REASONING WITH SPACE AND TIME Introduction Spatial Reasoning Spatial Relationships among Components of an Object Fuzzy Spatial Relationships among Objects Temporal Reasoning by Situation Calculus Propositional Temporal Logic Interval Temporal Logic Reasoning with Both Space and Time Conclusions INTELLIGENT PLANNING Introduction Planning with If-Add-Delete Operators Least Commitment Planning Hierarchical Task Network Planning Multi-agent Planning The Flowshop Scheduling Problem Summary MACHINE LEARNING TECHNIQUES Introduction Supervised Learning Unsupervised Learning Reinforcement Learning Learning by Inductive Logic Programming Computational Learning Theory Summary MACHINE LEARNING USING NEURAL NETS Biological Neural Nets Artificial Neural Nets Topology of Artificial Neural Nets Learning Using Neural Nets The Back-Propagation Training Algorithm Widrow-Hoff's Multi-Layers ADALINE Models Hopfield Neural Net Associative Memory Fuzzy Neural Nets Self-Organizing Neural Net Adaptive Resonance Theory (ART) Applications of Artificial Neural Nets GENETIC ALGORITHMS Introduction Deterministic Explanation of Holland's Observation Stochastic Explanation of GA The Markov Model for Convergence Analysis Application of GA in Optimization Problems Application of GA in Machine Learning Applications of GA in Intelligent Search Genetic Programming Conclusions REALIZING COGNITION USING FUZZY NEURAL NETS Cognitive Maps Learning by a Cognitive Map The Recall in a Cognitive Map Stability Analysis Cognitive Learning with FPN Applications in Autopilots Generation of Control Commands by a Cognitive Map Task Planning and Coordination Putting it all Together Conclusions and Future Directions VISUAL PERCEPTION Introduction Low level Vision Medium Level Vision High Level Vision Conclusions LINGUISTIC PERCEPTION Introduction Syntactic Analysis Augmented Transition Network Parsers Semantic Interpretation by Case Grammar and Type Hierarchy Discourse and Pragmatic Analysis Applications of Natural Language Understanding PROBLEM SOLVING BY CONSTRAINT SATISFACTION Introduction Formal Definitions Constraint Propagation in Networks Determining Satisfiability of CSP Constraint Logic Programming Geometric Constraint Satisfaction Conclusions ACQUISITION OF KNOWLEDGE Introduction Manual Approach for Knowledge Acquisition Knowledge Fusion from Multiple Experts Machine Learning Approach for Knowledge Acquisition Knowledge Refinement by Hebbian Learning Conclusions VALIDATION, VERIFICATION AND MAINTENANCE ISSUES Introduction Valildation of Expert Systems Verification of Knowledge Based System Maintenance of Knowledge Based Systems Conclusions PARALLEL AND DISTRIBUTED ARCHITECTURE FOR INTELLIGENT SYSTEMS Introduction Salient Features of AI Machines Parallelism in Heuristic Search Parallelism at Knowledge Representational Level Parallel Architecture for Logic Programming Conclusions CASE STUDY I: BUILDING A SYSTEM FOR CRIMINAL INVESTIGATION An Overview of the Proposed Scheme Introduction to Image Matching Fingerprint Classification and Matching Identification of the Suspects from Voice Identification of the Suspects from Incidental Descriptions Conclusions CASE STUDY II: REALIZATION OF COGNITION FOR MOBILE ROBOTS Mobile Robots Scope of Realization of Cognition on Mobile Robots Knowing the Robot's World Types of Navigational Planning Problems Offline Planning by Generalized Voronoi Diagram (GVD) Path Traversal Optimization Problem Self-Orgainizing Map (SOM) Online Navigation by Modular Back-Propagation Neural Nets Coordination among Sub-Modules in a Mobile Robot An Application in a Soccer Playing Robot The Expectations from the Readers APPENDIX A: How to Run the Sample Programs? APPENDIX B: Derivation of the Back-propagation Algorithm APPENDIX C: Proof of the Theorems of
• Chapter 10 INDEX.
• (source: Nielsen Book Data)

With all the material available in the field of artificial intelligence (AI) and soft computing-texts, monographs, and journal articles-there remains a serious gap in the literature. Until now, there has been no comprehensive resource accessible to a broad audience yet containing a depth and breadth of information that enables the reader to fully understand and readily apply AI and soft computing concepts. "Artificial Intelligence and Soft Computing" fills this gap. It presents both the traditional and the modern aspects of AI and soft computing in a clear, insightful, and highly comprehensive style. It provides an in-depth analysis of mathematical models and algorithms and demonstrates their applications in real world problems.Beginning with the behavioral perspective of "human cognition, " the text covers the tools and techniques required for its intelligent realization on machines. The author addresses the classical aspects-search, symbolic logic, planning, and machine learning-in detail and includes the latest research in these areas. He introduces the modern aspects of soft computing from first principles and discusses them in a manner that enables a beginner to grasp the subject. He also covers a number of other leading aspects of AI research, including nonmonotonic and spatio-temporal reasoning, knowledge acquisition, and much more. "Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of the Human Brain" is unique for its diverse content, clear presentation, and overall completeness. It provides a practical, detailed introduction that will prove valuable to computer science practitioners and students as well as to researchers migrating to the subject from other disciplines.
(source: Nielsen Book Data)

• PREFACE ACKNOWLEDGEMENT CHAPTER 1 INTRODUCTION: MULTI-AGENT COORDINATION BY REINFORCEMENT LEARNING AND EVOLUTIONARY ALGORITHMS 1 1.1 INTRODUCTION 2 1.2 SINGLE AGENT PLANNING 3 1.2.1 Terminologies used in single agent planning 4 1.2.2 Single agent search-based planning algorithms 9 1.2.2.1 Dijkstra's algorithm 10 1.2.2.2 A* (A-star) Algorithm 12 1.2.2.3 D* (D-star) Algorithm 14 1.2.2.4 Planning by STRIPS-like language 16 1.2.3 Single agent reinforcement learning 16 1.2.3.1 Multi-Armed Bandit Problem 17 1.2.3.2 Dynamic programming and Bellman equation 19 1.2.3.3 Correlation between reinforcement learning and Dynamic programming 20 1.2.3.4 Single agent Q-learning 20 1.2.3.5 Single agent planning using Q-learning 23 1.3 MULTI-AGENT PLANNING AND COORDINATION 24 1.3.1 Terminologies related to multi-agent coordination 24 1.3.2 Classification of multi-agent system 25 1.3.3 Game theory for multi-agent coordination 27 1.3.3.1 Nash equilibrium (NE) 30 1.3.3.1.1 Pure strategy NE (PSNE) 31 1.3.3.1.2 Mixed strategy NE (MSNE) 33 1.3.3.2 Correlated equilibrium (CE) 36 1.3.3.3 Static game examples 37 1.3.4 Correlation among RL, DP, and GT 39 1.3.5 Classification of MARL 39 1.3.5.1 Cooperative multi-agent reinforcement learning 41 1.3.5.1.1 Static 41 Independent Learner (IL) and Joint Action Learner (JAL) 41Frequency maximum Q-value (FMQ) heuristic 44 1.3.5.1.2 Dynamic 46 Team-Q 46 Distributed -Q 47 Optimal Adaptive Learning 50 Sparse cooperative Q-learning (SCQL) 52 Sequential Q-learning (SQL) 53 Frequency of the maximum reward Q-learning (FMRQ) 53 1.3.5.2 Competitive multi-agent reinforcement learning 55 1.3.5.2.1 Minimax-Q Learning 55 1.3.5.2.2 Heuristically-accelerated multi-agent reinforcement learning 56 1.3.5.3 Mixed multi-agent reinforcement learning 57 1.3.5.3.1 Static 57 Belief-based Learning rule 57 Fictitious play 57 Meta strategy 58 Adapt When Everybody is Stationary, Otherwise Move to Equilibrium (AWESOME) 60 Hyper-Q 62 Direct policy search based 63 Fixed learning rate 63 Infinitesimal Gradient Ascent (IGA) 63 Generalized Infinitesimal Gradient Ascent (GIGA) 65 Variable learning rate 66 Win or Learn Fast-IGA (WoLF-IGA) 66 GIGA-Win or Learn Fast (GIGA-WoLF) 66 1.3.5.3.2 Dynamic 67 Equilibrium dependent 67 Nash-Q Learning 67 Correlated-Q Learning (CQL) 68 Asymmetric-Q Learning (AQL) 68 Friend-or-Foe Q-learning 70 Negotiation-based Q-learning 71 MAQL with equilibrium transfer 74 Equilibrium independent 76 Variable learning rate 76 Win or Learn Fast Policy hill-climbing (WoLF-PHC) 76 Policy Dynamic based Win or Learn Fast (PD-WoLF) 78 Fixed learning rate 78 Non-Stationary Converging Policies (NSCP) 78 Extended Optimal Response Learning (EXORL) 79 1.3.6 Coordination and planning by MAQL 80 1.3.7 Performance analysis of MAQL and MAQL-based coordination 81 1.4 COORDINATION BY OPTIMIZATION ALGORITHM 83 1.4.1 Particle Swarm Optimization (PSO) Algorithm 84 1.4.2 Firefly Algorithm (FA) 87 1.4.2.1 Initialization 87 1.4.2.2 Attraction to Brighter Fireflies 87 1.4.2.3 Movement of Fireflies 88 1.4.3 Imperialist Competitive Algorithm (ICA) 89 1.4.3.1 Initialization 89 1.4.3.2 Selection of Imperialists and Colonies 89 1.4.3.3 Formation of Empires 89 1.4.3.4 Assimilation of Colonies 90 1.4.3.5 Revolution 91 1.4.3.6 Imperialistic Competition 91 1.4.3.6.1 Total Empire Power Evaluation 91 1.4.3.6.2 Reassignment of Colonies and Removal of Empire 92 1.4.3.6.3 Union of Empires 92 1.4.4 Differential evolutionary (DE) algorithm 93 1.4.4.1 Initialization 93 1.4.4.2 Mutation 93 1.4.4.3 Recombination 93 1.4.4.4 Selection 93 1.4.5 Offline optimization 94 1.4.6 Performance analysis of optimization algorithms 94 1.4.6.1 Friedman test 94 1.4.6.2 Iman-Davenport test 95 1.5 SCOPE OF THE Book 95 1.6 SUMMARY 98 References 98 CHAPTER 2 IMPROVE CONVERGENCE SPEED OF MULTI-AGENT Q-LEARNING FOR COOPERATIVE TASK-PLANNING 107 2.1 INTRODUCTION 108 2.2 LITERATURE REVIEW 112 2.3 PRELIMINARIES 114 2.3.1 Single agent Q-learning 114 2.3.2 Multi-agent Q-learning 115 2.4 PROPOSED MULTI-AGENT Q-LEARNING 118 2.4.1 Two useful properties 119 2.5 PROPOSED FCMQL ALGORITHMS AND THEIR CONVERGENCE ANALYSIS 120 2.5.1 Proposed FCMQL algorithms 120 2.5.2 Convergence analysis of the proposed FCMQL algorithms 121 2.6 FCMQL-BASED COOPERATIVE MULTI-AGENT PLANNING 122 2.7 EXPERIMENTS AND RESULTS 123 2.8 CONCLUSIONS 130 2.9 SUMMARY 131 2.10 APPENDIX 2.1 131 2.11 APPENDIX 2.2 135 References 152 CHAPTER 3 CONSENSUS Q-LEARNING FOR MULTI-AGENT COOPERATIVE PLANNING 157 3.1 INTRODUCTION 158 3.2 PRELIMINARIES 159 3.2.1 Single agent Q-learning 159 3.2.2 Equilibrium-based multi-agent Q-learning 160 3.3 CONSENSUS 161 3.4 PROPOSED CONSENSUS Q-LEARNING AND PLANNING 162 3.4.1 Consensus Q-learning 162 3.4.2 Consensus-based multi-robot planning 164 3.5 EXPERIMENTS AND RESULTS 165 3.5.1 Experimental setup 165 3.5.2 Experiments for CoQL 165 3.5.3 Experiments for consensus-based planning 166 3.6 CONCLUSIONS 168 3.7 SUMMARY 168 References 168 CHAPTER 4 AN EFFICIENT COMPUTING OF CORRELATED EQUILIBRIUM FOR COOPERATIVE Q-LEARNING BASED MULTI-AGENT PLANNING 171 4.1 INTRODUCTION 172 4.2 SINGLE-AGENT Q-LEARNING AND EQUILIBRIUM BASED MAQL 175 4.2.1 Single Agent Q learning 175 4.2.2 Equilibrium based MAQL 175 4.3 PROPOSED COOPERATIVE MULTI-AGENT Q-LEARNING AND PLANNING 176 4.3.1 Proposed schemes with their applicability 176 4.3.2 Immediate rewards in Scheme-I and -II 177 4.3.3 Scheme-I induced MAQL 178 4.3.4 Scheme-II induced MAQL 180 4.3.5 Algorithms for scheme-I and II 182 4.3.6 Constraint QL-I/ QL-II(C ......................................................... 183 4.3.7 Convergence 183 Multi-agent planning 185 4.4 COMPLEXITY ANALYSIS 186 4.4.1 Complexity of Correlated Q-Learning 187 4.4.1.1 Space Complexity 187 4.4.1.2 Time Complexity 187 4.4.2 Complexity of the proposed algorithms 188 4.4.2.1 Space Complexity 188 4.4.2.2 Time Complexity 188 4.4.3 Complexity comparison 189 4.4.3.1 Space complexity 190 4.4.3.2 Time complexity 190 4.5 SIMULATION AND EXPERIMENTAL RESULTS 191 4.5.1 Experimental platform 191 4.5.1.1 Simulation 191 4.5.1.2 Hardware 192 4.5.2 Experimental approach 192 4.5.2.1 Learning phase 193 4.5.2.2 Planning phase 193 4.5.3 Experimental results 194 4.6 CONCLUSION 201 4.7 SUMMARY 202 4.8 APPENDIX 203 References 209 CHAPTER 5 A MODIFIED IMPERIALIST COMPETITIVE ALGORITHM FOR MULTI-AGENT STICK- CARRYING APPLICATION
• 213 5.1 INTRODUCTION 214 5.2 PROBLEM FORMULATION FOR MULTI-ROBOT STICK-CARRYING 219 5.3 PROPOSED HYBRID ALGORITHM 222 5.3.1 An Overview of Imperialist Competitive Algorithm (ICA) 222 5.3.1.1 Initialization 222 5.3.1.2 Selection of Imperialists and Colonies 223 5.3.1.3 Formation of Empires 223 5.3.1.4 Assimilation of Colonies 223 5.3.1.5 Revolution 224 5.3.1.6 Imperialistic Competition 224 5.3.1.6.1 Total Empire Power Evaluation 225 5.3.1.6.2 Reassignment of Colonies and Removal of Empire 225 5.3.1.6.3 Union of Empires 226 5.4 AN OVERVIEW OF FIREFLY ALGORITHM (FA) 226 5.4.1 Initialization 226 5.4.2 Attraction to Brighter Fireflies 226 5.4.3 Movement of Fireflies 227 5.5 PROPOSED IMPERIALIST COMPETITIVE FIREFLY ALGORITHM 227 5.5.1 Assimilation of Colonies 229 5.5.1.1 Attraction to Powerful Colonies 230 5.5.1.2 Modification of Empire Behavior 230 5.5.1.3 Union of Empires 230 5.6 SIMULATION RESULTS 232 5.6.1 Comparative Framework 232 5.6.2 Parameter Settings 232 5.6.3 Analysis on Explorative Power of ICFA 232 5.6.4 Comparison of Quality of the Final Solution 233 5.6.5 Performance Analysis 233 5.7 COMPUTER SIMULATION AND EXPERIMENT 240 5.7.1 Average total path deviation (ATPD) 240 5.7.2 Average Uncovered Target Distance (AUTD) 241 5.7.3 Experimental Setup in Simulation Environment 241 5.7.4 Experimental Results in Simulation Environment 242 5.7.5 Experimental Setup with Khepera Robots 244 5.7.6 Experimental Results with Khepera Robots 244 5.8 CONCLUSION 245 5.9 SUMMARY 247 5.10 APPENDIX 5.1 248 References 249 CHAPTER 6 CONCLUSIONS AND FUTURE DIRECTIONS 255 6.1 CONCLUSIONS 256 6.2 FUTURE DIRECTIONS 257.
• (source: Nielsen Book Data)

Discover the latest developments in multi-robot coordination techniques with this insightful and original resource Multi-Agent Coordination: A Reinforcement Learning Approach delivers a comprehensive, insightful, and unique treatment of the development of multi-robot coordination algorithms with minimal computational burden and reduced storage requirements when compared to traditional algorithms. The accomplished academics, engineers, and authors provide readers with both a high-level introduction to, and overview of, multi-robot coordination, and in-depth analyses of learning-based planning algorithms. You'll learn about how to accelerate the exploration of the team-goal and alternative approaches to speeding up the convergence of TMAQL by identifying the preferred joint action for the team. The authors also propose novel approaches to consensus Q-learning that address the equilibrium selection problem and a new way of evaluating the threshold value for uniting empires without imposing any significant computation overhead. Finally, the book concludes with an examination of the likely direction of future research in this rapidly developing field. Readers will discover cutting-edge techniques for multi-agent coordination, including: An introduction to multi-agent coordination by reinforcement learning and evolutionary algorithms, including topics like the Nash equilibrium and correlated equilibrium Improving convergence speed of multi-agent Q-learning for cooperative task planning Consensus Q-learning for multi-agent cooperative planning The efficient computing of correlated equilibrium for cooperative q-learning based multi-agent planning A modified imperialist competitive algorithm for multi-agent stick-carrying applications Perfect for academics, engineers, and professionals who regularly work with multi-agent learning algorithms, Multi-Agent Coordination: A Reinforcement Learning Approach also belongs on the bookshelves of anyone with an advanced interest in machine learning and artificial intelligence as it applies to the field of cooperative or competitive robotics.
(source: Nielsen Book Data)

• An Overview of Call Admission Control in Mobile Cellular Networks
• An Overview of Computational Intelligence Algorithms
• Call Management in a Cellular Mobile Network Using Fuzzy Comparators
• An Evolutionary Approach to Velocity and Traffic Sensitive Call Admission Control
• Call Admission Control Using Bio-geography Based Optimization
• Conclusions and Future Directions.

Call Admission Control (CAC) and Dynamic Channel Assignments (DCA) are important decision-making problems in mobile cellular communication systems. Current research in mobile communication considers them as two independent problems, although the former greatly depends on the resulting free channels obtained as the outcome of the latter. This book provides a solution to the CAC problem, considering DCA as an integral part of decision-making for call admission. Further, current technical resources ignore movement issues of mobile stations and fluctuation in network load (incoming calls) in the control strategy used for call admission. In addition, the present techniques on call admission offers solution globally for the entire network, instead of considering the cells independently. CAC here has been formulated by two alternative approaches. The first approach aimed at handling the uncertainty in the CAC problem by employing fuzzy comparators. The second approach is concerned with formulation of CAC as an optimization problem to minimize call drop, satisfying a set of constraints on feasibility and availability of channels, hotness of cells, and velocity and angular displacement of mobile stations. Evolutionary techniques, including Genetic Algorithm and Biogeography Based Optimization, have been employed to solve the optimization problems. The proposed approaches outperform traditional methods with respect to grade and quality of services.

• Introduction to Emotional Intelligence.- Mathematical Modeling and Analysis of Dynamical Systems.- Preliminaries on Image Processing.- Brain Imaging and Psycho-Pathological Studies on Self-Regulation of Emotion.- Fuzzy Models for Facial Expression-Based Emotion Recognition and Control.- Control of Mental Stability in Emotion-Logic Interactive Dynamics.- Multiple Emotions and their Chaotic Dynamics.- Processing of EEG Signal for Detection and Prediction of Emotion.- Applications and Future Directions of Emotional Intelligence.- Open Research Problems.
• (source: Nielsen Book Data)

Emotional Intelligence is a new discipline of knowledge, dealing with modeling, recognition and control of human emotions. The book Emotional Intelligence: A Cybernetic Approach, to the best of the authors' knowledge is a first comprehensive text of its kind that provides a clear introduction to the subject in a precise and insightful writing style. It begins with a philosophical introduction to Emotional Intelligence, and gradually explores the mathematical models for emotional dynamics to study the artificial control of emotion using music and videos, and also to determine the interactions between emotion and logic from the points of view of reasoning. The later part of the book covers the chaotic behavior of co-existing emotions under certain conditions of emotional dynamics. Finally, the book attempts to cluster emotions using electroencephalogram signals, and demonstrates the scope of application of emotional intelligence in several engineering systems, such as human-machine interfaces, psychotherapy, user assistance systems, and many others. The book includes nine chapters. Chapter 1 provides an introduction to the subject from a philosophical and psychological standpoint. It outlines the fundamental causes of emotion arousal, and typical characteristics of the phenomenon of an emotive experience. The relation between emotion and rationality of thoughts is also introduced here. Principles of natural regulation of emotions are discussed in brief, and the biological basis of emotion arousal using an affective neuro-scientific model is introduced next. Scope of mathematical modeling to study the dynamics of emotion is also discussed. Principles of controlling emotion by artificial means, and the effect of emotion modeling on human-machine interactions are also outlined.

• Chapter 1: Introduction to Human Memory and Learning Models
• 1.1 Introduction 2
• 1.2 Philosophical Contributions to Memory Research 4
• 1.2.1 Atkinson and Shiffrin's Model 4
• 1.2.2 Tveter's Model 6
• 1.2.3 Tulving's model 6
• 1.2.4 The Parallel and Distributed Processing (PDP) Approach 8
• 1.2.5 Procedural and Declarative Memory 9
• 1.3 Brain-theoretic Interpretation of Memory Formation 11
• 1.3.1 Coding for Memory 11
• 1.3.2 Memory Consolidation 13
• 1.3.3 Location of stored Memories 16
• 1.3.4 Isolation of Information in Memory 16
• 1.4 Cognitive Maps 17
• 1.5 Neural Plasticity 18
• 1.6 Modularity 19
• 1.7 The cellular Process behind STM Formation 20
• 1.8 LTM Formation 21
• 1.9 Brain Signal Analysis in the Context of Memory and Learning 22
• 1.9.1 Association of EEG alpha and theta band with memory performances 22
• 1.9.2 Oscillatory beta and gamma frequency band activation in STM performance 26
• 1.9.3 Change in EEG band power with changing working memory load 26
• 1.9.4 Effects of Electromagnetic field on the EEG response of Working Memory 29
• 1.9.5 EEG Analysis to discriminate focused attention and WM performance 30
• 1.9.6 EEG power changes in memory repetition effect 31
• 1.9.7 Correlation between LTM Retrieval and EEG features 34
• 1.9.8 Impact of math anxiety on WM response: An EEG study 37
• 1.10 Memory Modelling by Computational Intelligence Techniques 38
• 1.11 Scope of the Book 43
• References 47
• Chapter 2: Working Memory Modeling Using Inverse Fuzzy Relational Approach
• 2.1 Introduction 56
• 2.2 Problem Formulation and Approach 59
• 2.2.1 Independent Component Analysis as a Source Localization Tool 61
• 2.2.2 Independent Component Analysis vs Principal Component Analysis 62
• 2.2.3 Feature Extraction 63
• 2.2.4 Phase 1: WM Modeling 64
• 2.2.4.1 Step I: WM modeling of subject using EEG signals during full face encoding and recall from specific part of same face 65
• 2.2.4.2 Step II: WM modeling of subject using EEG signals during full face encoding and recall from all parts of same face 68
• 2.2.5 Phase 2: WM Analysis 69
• 2.2.6 Finding Max-Min Compositional of Weight Matrix 70
• 2.3 Experimental Results and Performance Analysis 75
• 2.3.1 Experimental Set-up 75
• 2.3.2 Source Localization using e-LORETA 78
• 2.3.3 Pre-processing 79
• 2.3.4 Selection of EEG Features 80
• 2.3.5 WM Model Consistency across Partial Face Stimuli 81
• 2.3.6 Inter-person Variability of Weight Matrix W 85
• 2.3.7 Variation in Imaging Attributes 87
• 2.3.8 Comparative Analysis with existing Fuzzy Inverse Relations 87
• 2.4 Discussion 88
• 2.5 Conclusion 89
• References 90
• Chapter 3: Short-Term Memory Modeling in Shape-Recognition Task by Type-2 Fuzzy Deep Brain Learning
• 3.1 Introduction 98
• 3.2 System Overview 101
• 3.3 Brain Functional Mapping using Type-2 Fuzzy DBLN 107
• 3.3.1 Overview of Type-2 Fuzzy Sets 107
• 3.3.2 Type-2 Fuzzy Mapping and Parameter Adaptation by Perceptron-like Learning 108
• 3.3.2.1 Construction of the Proposed Interval Type-2 Fuzzy Membership Function 109
• 3.3.2.2 Construction of IT2FS Induced Mapping Function 110
• 3.3.2.3 Secondary Membership Function Computation of Proposed GT2FS 112
• 3.3.2.4 Proposed General Type-2 Fuzzy Mapping 114
• 3.3.3 Perceptron-like Learning for Weight Adaptation 115
• 3.3.4 Training of the Proposed Shape-Reconstruction Algorithm 116
• 3.3.5 The Test Phase of the Memory Model 118
• 3.4 Experiments and Results 118
• 3.4.1 Experimental Set-up 118
• 3.4.2 Experiment 1: Validation of the STM Model with respect to Error Metric 121
• 3.4.3 Experiment 2: Similar Encoding by a Subject for Similar Input Object-Shapes 122
• 3.4.4 Experiment 3: Study of Subjects' Learning Ability with Increasing Complexity in Object Shape 123
• 3.4.5 Experiment 4: Convergence Time of the Weight Matrix G for Increased Complexity of the Input Shape Stimuli 124
• 3.4.6 Experiment 5: Abnormality in G matrix for the subjects with Brain Impairment 125
• 3.5 Biological Implications 126
• 3.6 Performance Analysis 128
• 3.6.1 Performance Analysis of the Proposed T2FS Methods 128
• 3.6.2 Computational Performance Analysis of the Proposed T2FS Methods 130
• 3.6.3 Statistical Validation using Wilcoxon Signed-Rank Test 130
• 3.6.4 Optimal Parameter Selection and Robustness Study 131
• 3.7 Conclusions 133
• References 135
• Chapter 4: EEG Analysis for Subjective Assessment of Motor Learning Skill in Driving Using Type-2 Fuzzy Reasoning
• 4.1 Introduction 142
• 4.2 System Overview 144
• 4.2.1 Rule Design to determine the degree of learning 145
• 4.2.2 Single Trial Detection of Brain Signals 148
• 4.2.2.1 Feature Extraction 149
• 4.2.2.2 Feature Selection 149
• 4.2.2.3 Classification 150
• 4.2.3 Type-2 Fuzzy Reasoning 151
• 4.2.4 Training and Testing of the Classifiers 151
• 4.3 Determining Type and Degree of Learning by Type-2 Fuzzy Reasoning 151
• 4.3.1 Preliminaries on IT2FS and GT2FS 153
• 4.3.2 Proposed Reasoning Method 1: CIT2FS based Reasoning 153
• 4.3.3 Computation of Percentage Normalized Degree of Learning 155
• 4.3.4 Optimal Selection in IT2FS Reasoning 156
• 4.3.5 Proposed Reasoning Method 2: Triangular Vertical Slice Based CGT2FS Reasoning 156
• 4.3.6 Proposed Reasoning Method 3: CGT2FS Reasoning with Gaussian Secondary Membership Function (MF) 158
• 4.4 Experiments and Results 162
• 4.4.1 The Experimental set-up 162
• 4.4.2 Stimulus Presentation 163
• 4.4.3 Experiment 1: Source Localization using eLORETA 163
• 4.4.4 Experiment 2: Validation of the Rules 164
• 4.4.5 Experiment 3: Pre-processing and Artifact Removal using ICA 165
• 4.4.6 Experiment 4: N400 Old/New Effect Observation over the Successive Trials 167
• 4.4.7 Experiment 5: Selection of the Discriminating EEG Features using PCA 168
• 4.5 Performance Analysis and Statistical Validation 169
• 4.5.1 Performance Analysis of the LSVM Classifiers 169
• 4.5.2 Robustness Study 170
• 4.5.3 Performance Analysis of the Proposed T2FS Reasoning Methods 170
• 4.5.4 Computational Performance Analysis of the Proposed T2FS Reasoning Methods 171
• 4.5.5 Statistical Validation using Wilcoxon Signed-Rank Test 172
• 4.6 Conclusion 173
• References 173
• Chapter 5: EEG Analysis to Decode Human Memory Responses in Face Recognition Task Using Deep LSTM Network
• 5.1 Introduction 182
• 5.2 CSP Modeling 186
• 5.2.1 The Standard CSP Algorithm 186
• 5.2.2 The Proposed CSP Algorithm 187
• 5.3 Proposed LSTM Classifier with Attention Mechanism 189
• 5.4 Experiment and Results 195
• 5.4.1 The Experimental Set-up 195
• 5.4.2 Experiment 1: Activated Brain Region Selection using eLORETA 196
• 5.4.3 Experiment 2: Detection of the ERP signals associated with the familiar andunfamiliar face discrimination 198
• 5.4.4 Experiment 3: Performance Analysis of the Proposed CSP algorithm as a Feature extraction Technique 199
• 5.4.5 Experiment 4: Performance Analysis of the Proposed LSTM based Classifier 201
• 5.4.6 Experiment 5: Classifier Performance Analysis with varying EEG Time-Window Length 202
• 5.4.7 Statistical Validation of the Proposed LSTM Classifier using McNamers' Test 203
• 5.5 Conclusions 204
• References 204
• Chapter 6: Cognitive Load Assessment in Motor Learning Tasks by Near-Infrared Spectroscopy Using Type-2 Fuzzy Sets
• 6.1 Introduction 214
• 6.2 Principles and Methodologies 216
• 6.2.1 Normalization of Raw Data 217
• 6.2.2 Pre-processing 218
• 6.2.3 Feature Extraction 218
• 6.2.4 Training Instance Generation for Offline Training 219
• 6.2.5 Feature Selection using Evolutionary Algorithm 219
• 6.2.6 Classifier Training and Testing 221
• 6.3 Classifier Design 221
• 6.3.1 Preliminaries of IT2FS and GT2FS 221
• 6.3.2 IT2FS Induced Classifier Design 222
• 6.3.3 GT2FS Induced Classifier Design 228
• 6.4 Experiments and Results 230
• 6.4.1 Experimental Set-up 230
• 6.4.2 Participants 232
• 6.4.3 Stimulus Presentation for Online Classification 232
• 6.4.4 Experiment 1: Demonstration of decreasing Cognitive Load with increasing Learning Epochs for similar stimulus 233
• 6 .4.5 Experiment 2: Automatic Extraction of Discriminating fNIRs features 234
• 6.4.6 Experiment 3: Optimal Parameter Setting of Feature Selection and Classifier Units 235
• 6.5 Biological Implications 237
• 6.6 Performance Analysis 239
• 6.6.1 Performance Analysis of the proposed IT2FS and GT2FS Classifier 239
• 6.6.2 Statistical Validation of the Classifiers using McNamer--s Test 242
• 6.7 Conclusion 243
• References 243
• Chapter 7: Conclusions and Future Directions of Research on BCI based Memory and Learning
• 7.1 Self-Review of the Works Undertaken in the Book 250
• 7.2 Limitations of EEG BCI-Based Memory Experiments 252
• 7.3 Further Scope of Future Research on Memory and Learning 253
• References.
• (source: Nielsen Book Data)

Proposes computational models of human memory and learning using a brain-computer interfacing (BCI) approach Human memory modeling is important from two perspectives. First, the precise fitting of the model to an individual's short-term or working memory may help in predicting memory performance of the subject in future. Second, memory models provide a biological insight to the encoding and recall mechanisms undertaken by the neurons present in active brain lobes, participating in the memorization process. This book models human memory from a cognitive standpoint by utilizing brain activations acquired from the cortex by electroencephalographic (EEG) and functional near-infrared-spectroscopic (f-NIRs) means. Cognitive Modeling of Human Memory and Learning A Non-invasive Brain-Computer Interfacing Approach begins with an overview of the early models of memory. The authors then propose a simplistic model of Working Memory (WM) built with fuzzy Hebbian learning. A second perspective of memory models is concerned with Short-Term Memory (STM)-modeling in the context of 2-dimensional object-shape reconstruction from visually examined memorized instances. A third model assesses the subjective motor learning skill in driving from erroneous motor actions. Other models introduce a novel strategy of designing a two-layered deep Long Short-Term Memory (LSTM) classifier network and also deal with cognitive load assessment in motor learning tasks associated with driving. The book ends with concluding remarks based on principles and experimental results acquired in previous chapters. Examines the scope of computational models of memory and learning with special emphasis on classification of memory tasks by deep learning-based models Proposes two algorithms of type-2 fuzzy reasoning: Interval Type-2 fuzzy reasoning (IT2FR) and General Type-2 Fuzzy Sets (GT2FS) Considers three classes of cognitive loads in the motor learning tasks for driving learners Cognitive Modeling of Human Memory and Learning A Non-invasive Brain-Computer Interfacing Approach will appeal to researchers in cognitive neuro-science and human/brain-computer interfaces. It is also beneficial to graduate students of computer science/electrical/electronic engineering. .
(source: Nielsen Book Data)

• Metaheuristic Pattern Clustering - An Overview.- Differential Evolution Algorithm: Foundations and Perspectives.- Modeling and Analysis of the Population-Dynamics of Differential Evolution Algorithm.- Automatic Hard Clustering Using Improved Differential Evolution Algorithm.- Fuzzy Clustering in the Kernel-Induced Feature Space Using Differential Evolution Algorithm.- Clustering Using Multi-objective Differential Evolution Algorithms.- Conclusions and Future Research.
• (source: Nielsen Book Data)

Cluster analysis means the organization of an unlabeled collection of objects or patterns into separate groups based on their similarity. The task of computerized data clustering has been approached from diverse domains of knowledge like graph theory, multivariate analysis, neural networks, fuzzy set theory, and so on. Clustering is often described as an unsupervised learning method but most of the traditional algorithms require a prior specification of the number of clusters in the data for guiding the partitioning process, thus making it not completely unsupervised. Modern data mining tools that predict future trends and behaviors for allowing businesses to make proactive and knowledge-driven decisions, demand fast and fully automatic clustering of very large datasets with minimal or no user intervention. In this volume, we formulate clustering as an optimization problem, where the best partitioning of a given dataset is achieved by minimizing/maximizing one (single-objective clustering) or more (multi-objective clustering) objective functions. Using several real world applications, we illustrate the performance of several metaheuristics, particularly the Differential Evolution algorithm when applied to both single and multi-objective clustering problems, where the number of clusters is not known beforehand and must be determined on the run. This volume comprises of 7 chapters including an introductory chapter giving the fundamental definitions and the last Chapter provides some important research challenges. Academics, scientists as well as engineers engaged in research, development and application of optimization techniques and data mining will find the comprehensive coverage of this book invaluable.
(source: Nielsen Book Data)

• An Introduction to Logic Programming.- Parallel and Distributed Models for Logic Programming - A Review.- The Petri Net Model - A New Approach.- Realization of a Parallel Architecture for the Petri Net Model.- Parsing and Task Assignment on to the Proposed Parallel Architecture.- Logic Programming in Database Applications.
• (source: Nielsen Book Data)

This book introduces the parallel and distributed approach to logic programming, examining existing models of distributed logic programming, and proposing an alternative framework for distributed logic programming using extended Petri nets. The hardwired realization of the Petri net based framework is presented in detail, and principles of mapping of a logic program on to the proposed framework are outlined. Finally, the book explores the scope of Petri net models in designing next-generation deductive database machines.
(source: Nielsen Book Data)

• An Introduction to Logic Programming.- Parallel and Distributed Models for Logic Programming - A Review.- The Petri Net Model - A New Approach.- Realization of a Parallel Architecture for the Petri Net Model.- Parsing and Task Assignment on to the Proposed Parallel Architecture.- Logic Programming in Database Applications.
• (source: Nielsen Book Data)

This book introduces the parallel and distributed approach to logic programming, examining existing models of distributed logic programming, and proposing an alternative framework for distributed logic programming using extended Petri nets. The hardwired realization of the Petri net based framework is presented in detail, and principles of mapping of a logic program on to the proposed framework are outlined. Finally, the book explores the scope of Petri net models in designing next-generation deductive database machines.
(source: Nielsen Book Data)

• Multi-Robot Systems.- Vision-Based Autonomous Robot Navigation.- Multi View and Multi Scale Image Based Visual Servo For Micromanipulation.- Path Planning in Dynamic Environments.- Intelligent Neurofuzzy Control of a Robotic Gripper.- Voronoi-Based Outdoor Traversable Region Modelling.- Using Visual Features for Building and Localizing within Topological Maps of Indoor Environments.- Intelligent Precision Motion Control.
• (source: Nielsen Book Data)

There exists quite a vast literature on mobile robots, covering fundamental principles on motion control and path-planning in indoor environments using ultrasonic/laser transducers. However, there is a scarcity of books/collective documents on vision based navigation of mobile robots and multi-agent systems. The book fills this gap and attempts to develop interesting models for vision based map building in indoor and outdoor environments, precise motion control, navigation in dynamic environment, and above all multi-agent co-operation of robots. The most important aspect of this book is that the principles and models introduced in the text are all field-tested, and thus can readily be used in solving real world problems, such as factory automation, disposal of nuclear wastes, landmine clearing and computerized surgery. Primarily meant for graduate students and researchers in robotics, the book is equally useful to interested audience of any discipline for its contents and simplicity in presentation style.
(source: Nielsen Book Data)