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• Translator's Foreword: Why I Translate So Intelligently: Translation Metis in the Era of Google Translate Acknowledgments Preface Introduction
• 1. g: Intelligence and Genetic Fate
• 2. The "Blue Brain"
• 3. Like a Pollock Painting Conclusion Postscript to the English Translation. Artificial Intelligence: The Fourth Blow to Our Narcissism Notes Index.
• (source: Nielsen Book Data)

What is intelligence? The concept crosses and blurs the boundaries between natural and artificial, bridging the human brain and the cybernetic world of AI. In this book, the acclaimed philosopher Catherine Malabou ventures a new approach that emphasizes the intertwined, networked relationships among the biological, the technological, and the symbolic. Malabou traces the modern metamorphoses of intelligence, seeking to understand how neurobiological and neurotechnological advances have transformed our view. She considers three crucial developments: the notion of intelligence as an empirical, genetically based quality measurable by standardized tests; the shift to the epigenetic paradigm, with its emphasis on neural plasticity; and the dawn of artificial intelligence, with its potential to simulate, replicate, and ultimately surpass the workings of the brain. Malabou concludes that a dialogue between human and cybernetic intelligence offers the best if not the only means to build a democratic future. A strikingly original exploration of our changing notions of intelligence and the human and their far-reaching philosophical and political implications, Morphing Intelligence is an essential analysis of the porous border between symbolic and biological life at a time when once-clear distinctions between mind and machine have become uncertain.
(source: Nielsen Book Data)

• The Process of Industrial Bioethanol Production Explained by Self-Organised Maps / Miguel A. Sanz-Bobi, Pablo Ruiz and Julio Montes
• Towards a Further Understanding of the Robotic Darwinian PSO / Micael S. Couceiro, Fernando M. L. Martins, Filipe Clemente, Rui P. Rocha and Nuno M. F. Ferreira
• A Comparison Study Between Two Hyperspectral Clustering Methods: KFCM and PSO-FCM / Amin Alizadeh Naeini, Saeid Niazmardi, Shahin Rahmatollahi Namin, Farhad Samadzadegan and Saeid Homayouni
• Comparison of Classification Methods for Golf Putting Performance Analysis / J. Miguel A. Luz, Micael S. Couceiro, David Portugal, Rui P. Rocha and Hélder Araújo, et al.
• Switched Unfalsified Multicontroller Nonparametric Model Based Design / Fernando Coito, Luís Brito Palma and Fernando Costa
• Evolving Fuzzy Uncalibrated Visual Servoing for Mobile Robots / P. J. S. Gonçalves, P. J. F. Lopes, P. M. B. Torres and J. M. R. Sequeira

• Evaluating the Potential of Particle Swarm Optimization for Hyperspectral Image Clustering in Minimum Noise Fraction Feature Space / Shahin Rahmatollahi Namin, Amin Alizadeh Naeini and Farhad Samadzadegan
• On a Ball's Trajectory Model for Putting's Evaluation / Gonçalo Dias, Rui Mendes, Micael S. Couceiro, Carlos M. Figueiredo and J. Miguel A. Luz
• Efficient Discriminative Models for Proteomics with Simple and Optimized Features / Lionel Morgado, Carlos Pereira, Paula Veríssimo and António Dourado
• Meta-heuristics Self-Parameterization in a Multi-agent Scheduling System Using Case-Based Reasoning / Ivo Pereira, Ana Madureira and Paulo de Moura Oliveira
• Haptic-Based Robot Teleoperation: Interacting with Real Environments / Pedro Neto, Nélio Mourato and J. Norberto Pires
• Multi-agent Predictive Control with Application in Intelligent Infrastructures / J. M. Igreja, S. J. Costa, J. M. Lemos and F. M. Cadete
• Single-Objective Spreading Algorithm / E. J. Solteiro Pires, Luís Mendes, António M. Lopes, P. B. de Moura Oliveira and J. A. Tenreiro Machado

• Fault Tolerant Control Based on Adaptive LQG and Fuzzy Controllers / Carla Viveiros, Luis Brito Palma and José Manuel Igreja
• P2P Web Service Based System for Supporting Decision-Making in Cellular Manufacturing Scheduling / Maria Leonilde R. Varela, Rui Barbosa and Susana Costa
• Web-Based Decision Support System for Orders Planning / António Arrais-Castro, Maria Leonilde R. Varela and Sílvio Carmo-Silva
• Product Documentation Management Through REST-Based Web Service / Filipe Rocha, Maria Leonilde R. Varela and Sílvio Carmo-Silva
• Fuzzy Web Platform for Electrical Energy Losses Management / Gaspar Gonçalves Vieira, Maria Leonilde R. Varela and Rita A. Ribeiro
• Web System for Supporting Project Management / Cátia Filipa Veiga Alves, André Filipe Nogueira da Silva and Maria Leonilde R. Varela
• Generation Capacity Expansion Planning in Restructured Electricity Markets Using Genetic Algorithms / Adelino J. C. Pereira and João Tomé Saraiva

• Decision Making in Maintainability of High Risk Industrial Equipment / José Sobral and Luis Ferreira
• The Classification Platform Applied to Mammographic Images / P. J. S. Gonçalves
• On an Optimization Model for Approximate Nonnegative Matrix Factorization / Ana Maria de Almeida
• Random Walks in Electric Networks / D. M. L. D. Rasteiro
• Business Intelligence Tools / Jorge Bernardino and Marco Tereso
• Food Service Management Web Platform Based on XML Specification and Web Services / Pedro Sabioni, Vinícius Carneiro and Maria Leonilde R. Varela
• Differential Evolution on the Minimization of Thermal Residual Stresses in Functionally Graded Structures / T. A. N. Silva and M. A. R. Loja
• Magnetic Wheeled Climbing Robot: Design and Implementation / M. F. Silva, R. S. Barbosa and A. L. C. Oliveira
• Development of an AGV Controlled by Fuzzy Logic / Ramiro S. Barbosa, Manuel F. Silva and Dário J. Osório
• Affect Recognition / Raquel Faria and Ana Almeida
• Web 2.0: Tagging Usefulness / Joaquim Filipe P. Santos and Ana Almeida

• Multidimensional Scaling Analysis of Electricity Market Prices / Filipe Azevedo and J. Tenreiro Machado
• PCMAT Metadata Authoring Tool / Paulo Couto, Constantino Martins, Luiz Faria, Marta Fernandes and Eurico Carrapatoso
• Collaborative Broker for Distributed Energy Resources / João Carlos Ferreira, Alberto Rodrigues da Silva, Vítor Monteiro and João L. Afonso
• A Multidimensional Scaling Classification of Robotic Sensors / Miguel F. M. Lima and J. A. Tenreiro Machado
• Rough Set Theory: Data Mining Technique Applied to the Electrical Power System / C. I. Faustino Agreira, C. M. Machado Ferreira and F. P. Maciel Barbosa
• Tuning a Fractional Order Controller from a Heat Diffusion System Using a PSO Algorithm / Isabel S. Jesus and Ramiro S. Barbosa
• A Tool for Biomedical - Documents Classification Using Support Vector Machines / João Oliveira, Daniel Correia, Carlos Pereira, Paula Veríssimo and António Dourado

• Conflicts Management in Retail Systems with Self-Regulation / Bruno Magalhães and Ana Madureira
• Adaptive e-Learning Systems Foundational Issues of the ADAPT Project / Eduardo Pratas and Viriato M. Marques
• Recognizing Music Styles - An Approach Based on the Zipf-Mandelbrot Law / Viriato M. Marques and Cecília Reis
• A Platform for Peptidase Detection Based on Text Mining Techniques and Support Vector Machines / Daniel Correia, Carlos Pereira, Paula Veríssimo and António Dourado
• Optimal Configuration of Uniplanar-Unilateral External Fixators in Tibia Fractures / Luis Roseiro and Augusta Neto
• Identification of the Forces in the Suspension System of a Race Car Using Artificial Neural Networks / Luis Roseiro, Carlos Alcobia, Pedro Ferreira, Abderrahmane Baïri and Najib Laraqi, et al.
• Combinational Logic Circuits Design Tool for a Learning Management System / Cecília Reis and Viriato M. Marques
• Labeling Methods for the General Case of the Multi-objective Shortest Path Problem - A Computational Study / J. M. Paixão and J. L. Santos.

This book provides a general overview and original analysis of new developments and applications in several areas of Computational Intelligence and Information Systems. Computational Intelligence has become an important tool for engineers to develop and analyze novel techniques to solve problems in basic sciences such as physics, chemistry, biology, engineering, environment and social sciences. The material contained in this book addresses the foundations and applications of Artificial Intelligence and Decision Support Systems, Complex and Biological Inspired Systems, Simulation and Evolution of Real and Artificial Life Forms, Intelligent Models and Control Systems, Knowledge and Learning Technologies, Web Semantics and Ontologies, Intelligent Tutoring Systems, Intelligent Power Systems, Self-Organized and Distributed Systems, Intelligent Manufacturing Systems and Affective Computing. The contributions have all been written by international experts, who provide current views on the topics discussed and present recent, original insights from their own experience in these fields.
(source: Nielsen Book Data)

"A collection of critical essays dealing with the social and ethical impacts of AI including issues of trust, reliability, and bias"-- Provided by publisher.

"The first book that provides a systematic and rigorous logical theory of causality"-- Provided by publisher.

• Chap. 1 Rethinking the Human-Agent Relationship: Which Social Cues Do Interactive Agents Really Need to Have?.- Chap. 2 Believability Through Psychosocial Behaviour: Creating Bots That Are More Engaging and Entertaining.- Chap. 3 Actor Bots.- Chap. 4 Embodied Conversational Agent Avatars in Virtual Worlds.- Chap. 5 Human-Like Combat Behaviour via Multiobjective Neuroevolution.- Chap. 6 Believable Bot Navigation via Playback of Human Traces.- Chap. 7 A Machine Consciousness Approach to the Design of Human-Like Bots.- Chap. 8 ConsScale FPS: Cognitive Integration for Improved Believability in Computer Game Bots.- Chap. 9 Assessing Believability.- Chap. 10 Making Diplomacy Bots Individual.- Chap. 11 Towards Imitation of Human Driving Style in Car Racing Games.
• (source: Nielsen Book Data)

We share our modern world with bots - chatbots to converse with, roombots to clean our houses, spambots to fill our e-mail inboxes, and medibots to assist our surgeons. This book is about computer game bots, virtual companions who accompany us in virtual worlds or sharpen our fighting skills. These bots must be believable, that is human players should believe they are interacting with entities operating at a human level - bots are more fun if they behave like we do. This book shows how to create believable bots that play computer games, and it discusses the implications of making them appear human. The chapters in this book present the state of the art in research on and development of game bots, and they also look beyond the design aspects to address deep questions: Is a bot that plays like a person intelligent? Does it have emotions? Is it conscious? The topic is inherently interdisciplinary, and the work draws from research and practice in many fields, such as design, creativity, entertainment, and graphics; learning, psychology, and sociology; artificial intelligence, embodiment, agents, machine learning, robotics, human-computer interaction, and artificial life; cognition and neuroscience; and evolutionary computing. The contributing authors are among the leading researchers and developers in this field, and most of the examples and case studies involve analysis of commercial products. The book will be of value to graduate students and academic researchers in artificial intelligence, and to engineers charged with the design of entertaining games.
(source: Nielsen Book Data)

"This book examines artistic practices that use machine learning and computational technologies through historical perspectives surrounding adaptive systems from the 1950s onwards"-- Provided by publisher.

• Chapter 1 Introduction
• Chapter 2 Analysis Process
• Chapter 3 How it Works
• Chapter 4 Goals
• Chapter 5 Visitor Analysis
• Chapter 6 Traffic Analysis
• Chapter 7 How People Use Content
• Chapter 8 Clickpath Analysis
• Chapter 9 Segmentation
• Chapter 10 Putting it Together
• Chapter 11 Testing
• Chapter 12 Measuring Behavior within Pages
• Chapter 13 AB Testing
• Chapter 14 Profiles
• Chapter 15 Culture
• Chapter 16 Conclusion.
• (source: Nielsen Book Data)

Practical Web Analytics for User Experience teaches you how to use web analytics to help answer the complicated questions facing UX professionals. Within this book, you'll find a quantitative approach for measuring a website's effectiveness and the methods for posing and answering specific questions about how users navigate a website. The book is organized according to the concerns UX practitioners face. Chapters are devoted to traffic, clickpath, and content use analysis, measuring the effectiveness of design changes, including A/B testing, building user profiles based on search habits, supporting usability test findings with reporting, and more. This is the must-have resource you need to start capitalizing on web analytics and analyze websites effectively.
(source: Nielsen Book Data)

"Academic, industry, and government experts provide a global survey of the financial services sector's transformation by AI, data science, and blockchain"-- Provided by publisher.

• Introduction - Ethical and Sustainable Human and Artificial AI Artificial Intelligence and Robotics Applying The Benefits of AI and Identifying Challenges and Risks Starting AI - How to Build A Machine Learning Toolbox Algorithms The Management, Roles and Responsibilities of Humans and Machines AI in Use in Industry - Reimagining Everything in the Fourth Industrial Revolution AI Case Studies .
• (source: Nielsen Book Data)

In alignment with BCS AI Foundation and Essentials certificates, this introductory guide provides the understanding you need to start building artificial intelligence (AI) capability into your organisation. You will learn how AI is being utilised today and how it is likely to be used in the future to balance the talents of humans and machines. You will explore robotics and machine learning within the context of AI, and discover how the challenges AI presents are being addressed.
(source: Nielsen Book Data)

• Preface xv Acknowledgments xix About the Author xxi 1 Introduction to Reinforcement and Systemic Machine Learning 1 1
• .1. Introduction 1 1
• .2. Supervised, Unsupervised, and Semisupervised Machine Learning 2 1
• .3. Traditional Learning Methods and History of Machine Learning 4 1
• .4. What Is Machine Learning? 7 1
• .5. Machine-Learning Problem 8 1
• .6. Learning Paradigms 9 1
• .7. Machine-Learning Techniques and Paradigms 12 1
• .8. What Is Reinforcement Learning? 14 1
• .9. Reinforcement Function and Environment Function 16 1
• .10. Need of Reinforcement Learning 17 1
• .11. Reinforcement Learning and Machine Intelligence 17 1
• .12. What Is Systemic Learning? 18 1
• .13. What Is Systemic Machine Learning? 18 1
• .14. Challenges in Systemic Machine Learning 19 1
• .15. Reinforcement Machine Learning and Systemic Machine Learning 19 1
• .16. Case Study Problem Detection in a Vehicle 20 1
• .17. Summary 20 2 Fundamentals of Whole-System, Systemic, and Multiperspective Machine Learning 23 2
• .1. Introduction 23 2
• .2. What Is Systemic Machine Learning? 27 2
• .3. Generalized Systemic Machine-Learning Framework 30 2
• .4. Multiperspective Decision Making and Multiperspective Learning 33 2
• .5. Dynamic and Interactive Decision Making 43 2
• .6. The Systemic Learning Framework 47 2
• .7. System Analysis 52 2
• .8. Case Study: Need of Systemic Learning in the Hospitality Industry 54 2
• .9. Summary 55 3 Reinforcement Learning 57 3
• .1. Introduction 57 3
• .2. Learning Agents 60 3
• .3. Returns and Reward Calculations 62 3
• .4. Reinforcement Learning and Adaptive Control 63 3
• .5. Dynamic Systems 66 3
• .6. Reinforcement Learning and Control 68 3
• .7. Markov Property and Markov Decision Process 68 3
• .8. Value Functions 69 3.8
• .1. Action and Value 70 3
• .9. Learning an Optimal Policy (Model-Based and Model-Free Methods) 70 3
• .10. Dynamic Programming 71 3
• .11. Adaptive Dynamic Programming 71 3
• .12. Example: Reinforcement Learning for Boxing Trainer 75 3
• .13. Summary 75 4 Systemic Machine Learning and Model 77 4
• .1. Introduction 77 4
• .2. A Framework for Systemic Learning 78 4
• .3. Capturing the Systemic View 86 4
• .4. Mathematical Representation of System Interactions 89 4
• .5. Impact Function 91 4
• .6. Decision-Impact Analysis 91 4
• .7. Summary 97 5 Inference and Information Integration 99 5
• .1. Introduction 99 5
• .2. Inference Mechanisms and Need 101 5
• .3. Integration of Context and Inference 107 5
• .4. Statistical Inference and Induction 111 5
• .5. Pure Likelihood Approach 112 5
• .6. Bayesian Paradigm and Inference 113 5
• .7. Time-Based Inference 114 5
• .8. Inference to Build a System View 114 5
• .9. Summary 118 6 Adaptive Learning 119 6
• .1. Introduction 119 6
• .2. Adaptive Learning and Adaptive Systems 119 6
• .3. What Is Adaptive Machine Learning? 123 6
• .4. Adaptation and Learning Method Selection Based on Scenario 124 6
• .5. Systemic Learning and Adaptive Learning 127 6
• .6. Competitive Learning and Adaptive Learning 140 6
• .7. Examples 146 6
• .8. Summary 149 7 Multiperspective and Whole-System Learning 151 7
• .1. Introduction 151 7
• .2. Multiperspective Context Building 152 7
• .3. Multiperspective Decision Making and Multiperspective Learning 154 7
• .4. Whole-System Learning and Multiperspective Approaches 164 7
• .5. Case Study Based on Multiperspective Approach 167 7
• .6. Limitations to a Multiperspective Approach 174 7
• .7. Summary 174 8 Incremental Learning and Knowledge Representation 177 8
• .1. Introduction 177 8
• .2. Why Incremental Learning? 178 8
• .3. Learning from What Is Already Learned... 180 8
• .4. Supervised Incremental Learning 191 8
• .5. Incremental Unsupervised Learning and Incremental Clustering 191 8
• .6. Semisupervised Incremental Learning 196 8
• .7. Incremental and Systemic Learning 199 8
• .8. Incremental Closeness Value and Learning Method 200 8
• .9. Learning and Decision-Making Model 205 8
• .10. Incremental Classification Techniques 206 8
• .11. Case Study: Incremental Document Classification 207 8
• .12. Summary 208 9 Knowledge Augmentation: A Machine Learning Perspective 209 9
• .1. Introduction 209 9
• .2. Brief History and Related Work 211 9
• .3. Knowledge Augmentation and Knowledge Elicitation 215 9
• .4. Life Cycle of Knowledge 217 9
• .5. Incremental Knowledge Representation 222 9
• .6. Case-Based Learning and Learning with Reference to Knowledge Loss 224 9
• .7. Knowledge Augmentation: Techniques and Methods 224 9
• .8. Heuristic Learning 228 9
• .9. Systemic Machine Learning and Knowledge Augmentation 229 9
• .10. Knowledge Augmentation in Complex Learning Scenarios 232 9
• .11. Case Studies 232 9
• .12. Summary 235 10 Building a Learning System 237 10
• .1. Introduction 237 10
• .2. Systemic Learning System 237 10
• .3. Algorithm Selection 242 10
• .4. Knowledge Representation 244 10
• .5. Designing a Learning System 245 10
• .6. Making System to Behave Intelligently 246 10
• .7. Example-Based Learning 246 10
• .8. Holistic Knowledge Framework and Use of Reinforcement Learning 246 10
• .9. Intelligent Agents--Deployment and Knowledge Acquisition and Reuse 250 10
• .10. Case-Based Learning: Human Emotion-Detection System 251 10
• .11. Holistic View in Complex Decision Problem 253 10
• .12. Knowledge Representation and Data Discovery 255 10
• .13. Components 258 10
• .14. Future of Learning Systems and Intelligent Systems 259 10
• .15. Summary 259 Appendix A: Statistical Learning Methods 261 Appendix B: Markov Processes 271 Index 281.
• (source: Nielsen Book Data)

Reinforcement and Systemic Machine Learning for Decision Making explores a newer and growing avenue of machine learning algorithm in the area of computational intelligence. This book focuses on reinforcement and systemic learning to build a new learning paradigm, which makes effective use of these learning methodologies to increase machine intelligence and help us in building the advance machine learning applications. Illuminating case studies reflecting the authors' industrial experiences and pragmatic downloadable tutorials are available for researchers and professionals.
(source: Nielsen Book Data)

• Low-Level Image Processing Based on Interval-Valued Fuzzy Sets and Scale-Space Smoothing.- Generalized Net Representation of Dataflow Process Networks.- Wireless Sensor Positioning ACO Algorithm.- Time Accounting Artificial Neural Networks for Biochemical Process Models.- Periodic Time-varying Observer-based Learning Control of A/F Ratio in Multi-cylinder IC Engines.- Fuzzy T-S Model Based Design of Min-Max Control for Uncertain Nonlinear Systems.- Modeling Parallel Optimization of the Early Stopping Method of Multilayer Perceptron.- Intelligent Controls for Switched Fuzzy Systems: Synthesis via Non-standard Lyapunov Functions.- A New Architecture for an Adaptive Switching Controller Based on Hybrid Multiple T-S Models.- Optimization of Linear Objective Function under min-Probabilistic Sum Fuzzy Linear Equations Constraint.- Intuitionistic Fuzzy Logic Implementation to Assess Purposeful Model Parameters Genesis.- Dynamic Representation and Interpretation in a Multiagent 3D Tutoring System.- Generalized Net Model of the Scapulohumeral Rhythm.- Method for Interpretation of Functions of Propositional Logic by Specific Binary Markov Processes.- Generalized Net Models of Academic Promotion and Doctoral Candidature.- Modeling Telehealth Services with Generalized Nets.- State-Space Fuzzy-Neural Predictive Control.- Free Search and Particle Swarm Optimisation Applied to Global Optimisation Numerical Tests From Two to Hundred Dimensions.-Intuitionistic Fuzzy Sets Generated by Archimedean Metrics and Ultrametrics.-Production Rule and Network Structure Models for Knowledge Extraction from Complex Processes Under Uncertainty.
• (source: Nielsen Book Data)

This volume is a brief, yet comprehensive account of new development, tools, techniques and solutions in the broadly perceived "intelligent systems". New concepts and ideas concern the development of effective and efficient models which would make it possible to effectively and efficiently describe and solve processes in various areas of science and technology. Special emphasis is on the dealing with uncertainty and imprecision that permeates virtually all real world processes and phenomena, and has to properly be modeled by formal and algorithmic tools and techniques so that they be adequate and useful. The papers in this volume concern a wide array of possible techniques exemplified by, on the one hand, logic, probabilistic, fuzzy, intuitionistic fuzzy, neuro-fuzzy, etc. approaches. On the other hand, they represent the use of such systems modeling tools as generalized nets, optimization and control models, systems analytic models, etc. They concerns a variety of approaches, from pattern recognition, image analysis, education system modeling, biological and medical systems modeling, etc.
(source: Nielsen Book Data)

• Chapter 1: Requirements for Digital Preservation.-
• Chapter 2: Metadata in Long-term Digital Preservation.-
• Chapter 3: The CREDO Project.-
• Chapter 4: CREDO Repository Architecture.-
• Chapter 5: Information Processing in CREDO Long-term Archive.-
• Chapter 6: Metadata in CREDO Long-term Archive.-
• Chapter 7: Persistence Management in Long-term Digital Archive.-
• Chapter 8: Power Efficiency and Scheduling Access to the Archive.-
• Chapter 9: Information Management in Federated Digital Archives.
• (source: Nielsen Book Data)

This book addresses the process of maintaining digital objects through time to ensure continued access, an aspect that has become a crucial issue in recent years. It offers a concise yet comprehensive discussion of key concepts and requirements for long-term digital preservation, and presents a pioneering framework for digital repositories that enables the long-term archiving and metadata management for large volumes of digital resources based on a system that has already been completely designed and launched. In the framework, the reliability of information readouts is ensured by the repository with two-level data recording replication and monitoring mechanisms in the repository management system (RMS) and the file systems, and by the RMS's distributed nature. The advanced RMS allows operations on the archival storage to be scheduled, while also taking into account low energy consumption requirements. After presenting the framework in detail, the book assesses and demonstrates the approach's viability in terms of delivering accessibility, authenticity and usability. As such, the book offers a valuable resource for information technology (IT) researchers and practitioners, as well as archivists and librarians.
(source: Nielsen Book Data)

• Part I. Introduction;
• Chapter 1. Semantic Web; 1.1 Description Logics and Semantic Web; 1.2 The Current Vision of the Semantic Web;
• Chapter 2. Probability; 2.1 Probabilistic Inference; 2.2 Probabilistic Learning;
• Chapter 3. Aims of the Thesis;
• Chapter 4. Structure of the Thesis; Part II. Description Logics;
• Chapter 5. Foundations of Description Logics;
• Chapter 6. Description Logics' Characteristics; 6.1 Concept and Role Constructors; 6.2 Family of DLs; 6.3 Knowledge Base; 6.3.1 TBox; 6.3.2 RBox; 6.3.3 ABox; 6.4 Semantics.

• Chapter 7. Significant Examples of Description Logics;
• Chapter 8. OWL: the Web Ontology Language;
• Chapter 9. Inference in Description Logics; 9.1 Approaches to Compute Explanations; 9.1.1 Solving min-a-enum: The Standard Definition; 9.1.2 Resolving min-a-enum: Pinpointing Formula; Part III. A Probabilistic Semantics for Description Logics;
• Chapter 10. Distribution Semantics; 10.1 Formal Definition; 10.2 PLP Languages under the Distribution Semantics; 10.2.1 Logic Programming; 10.2.2 LPAD; 10.2.3 ProbLog; 10.3 Inference in Probabilistic Logic Programming; 10.3.1 ProbLog Inference System.

• 10.3.2 PITA; 10.4 Learning in Probabilistic Logic Programming;
• Chapter 11. DISPONTE;
• Chapter 12. Probabilistic Description Logics; Part IV. Inference in Probabilistic DLs;
• Chapter 13. Inference; 13.1 Splitting Algorithm; 13.2 Binary Decision Diagrams;
• Chapter 14. BUNDLE;
• Chapter 15. TRILL; 15.1 TRILL on SWISH;
• Chapter 16. TRILL P;
• Chapter 17. Complexity of Inference;
• Chapter 18. Related Inference Systems;
• Chapter 19. Experiments; 19.1 BUNDLE: Comparison with PRONTO; 19.2 BUNDLE: Not Entailed Queries; 19.3 BUNDLE: Inference with Limited Number of Explanations; 19.4 BUNDLE: Scalability.

• 19.5 TRILL, TRILL P & BUNDLE: Comparing Different Approaches; 19.6 Discussion; Part V. Learning in Probabilistic DLs;
• Chapter 20. Learning;
• Chapter 21. EDGE: Parameter Learning; 21.1 Expectation Maximization Algorithm; 21.2 EDGE;
• Chapter 22. LEAP: Structure Learning; 22.1 CELOE; 22.2 LEAP;
• Chapter 23. Distributed Learning; 23.1 Map Reduce Approach; 23.2 The Message Passing Interface Standard; 23.3 EDGE MR; 23.4 LEAP MR;
• Chapter 24. Related Learning Systems;
• Chapter 25. Experiments; 25.1 EDGE: Comparison with Association Rules; 25.2 LEAP & EDGE: a Comparison Between Different Learning Problems.

• 25.3 EDGE MR: Parallelization Speedup; 25.4 EDGE MR: Memory Consumption; 25.5 LEAP MR: Parallelization Speedup; 25.6 Discussion; Part VI. Summary and Future Work;
• Chapter 26. Conclusion;
• Chapter 27. Future Work.

• Machine generated contents note: Survey on Applying Machine Learning Techniques for Behavioral Awareness / Wouter Joosen
• Modelling Spatial and Temporal Context to Support Activity Recognition / Stephen Marsland
• Affect Aware Ambient Intelligence: Current and Future Directions / Anne James
• Behavioral Biometrics and Ambient Intelligence: New Opportunities for Context-Aware Applications / Paulo Novais
• Energy and Environmental Long-Term Monitoring System for Inhabitants' Weil-Being / Filippo Palumbo
• Behavioural Patterns from Cellular Data Streams and Outdoor Lighting as Strong Allies for Smart Urban Ecosystems / Adam Sedziwy
• Learning Daily Routines in Smart Office Environments / Stefano Ferilli
• EKRUCAmI Architecture -- Applications in Healthcare Domain / Hyun Yoe
• Qualitative Image Descriptor QIDL+N to Obtain Logics and Narratives Applied to Ambient Intelligent Systems / Zoe Falomir.

Annotation We are moving towards a future where environments respond to human preferences and needs. In this world, smart devices equipped with intelligent features and the capability to sense, communicate with and support humans in daily activities will be unremarkable. We already expect our cars to warn us of hazards, track our location and provide timely route advice, and in future we will speak to simple machines and hold conversations with more complex systems, such as intelligent homes, which will help us to monitor conditions, track routine tasks, and program the heating, lighting, garden watering and entertainment centre. But questions have been raised in recent years as to how intelligent these so called smart systems or ambient intelligence environments really are. This book, State of the Art in AI Applied to Ambient Intelligence, part of the outcome of the Workshop on Artificial Intelligence Techniques for Ambient Intelligence (AITAmI) which has now run for 10 consecutive editions, aims to provide a clear picture of what has been achieved after a decade of discussion. It is representative of the diversity of approaches and issues which are currently being considered, and also indicates those avenues which are the most promising for exploration in the next decade. The book provides all those working in the field with an up-to-date reference where they will find inspiration to create better systems for the society of tomorrow.

• Introduction and Related Work
• Optimal State Change Attempts
• Different Kinds of Effect Estimators
• A Comparison with Planning under Uncertainty
• Experimental Evaluation
• Conclusions.

This Springer Brief presents a basic algorithm that provides a correct solution to finding an optimal state change attempt, as well as an enhanced algorithm that is built on top of the well-known trie data structure. It explores correctness and algorithmic complexity results for both algorithms and experiments comparing their performance on both real-world and synthetic data. Topics addressed include optimal state change attempts, state change effectiveness, different kind of effect estimators, planning under uncertainty and experimental evaluation. These topics will help researchers analyze tabular data, even if the data contains states (of the world) and events (taken by an agent) whose effects are not well understood. Event DBs are omnipresent in the social sciences and may include diverse scenarios from political events and the state of a country to education-related actions and their effects on a school system. With a wide range of applications in computer science and the social sciences, the information in this Springer Brief is valuable for professionals and researchers dealing with tabular data, artificial intelligence and data mining. The applications are also useful for advanced-level students of computer science.

• Title Page
• Foreword
• Contents
• List of Contributors
• Part I. Software Engineering Meets Semantic Web: Concepts and Theories
• Motivation and Introduction
• Close Encounters of the Semantic Web and MDA Kinds
• Generating Model Transformations Using Ontology Engineering Space
• Part II. Realize Software Engineering by Semantic Web Technologies
• Towards a Consistent Feature Model using OWL
• Using Semantic Web Technologies for Management Application Integration
• Semantic Web Enabled Software Analysis
• Semantically Enabling Web Service Repositories

• ABC: A methodology for Semantic Web Application DevelopmentModel-driven Design Frameworks for Semantic Web Applications
• Part III. Design Ontologies for Software Engineering
• A Software Process Ontology and Its Application
• Enriching SE Ontologies with Bug Quality
• Learning Ontologies from Software Artifacts: Exploring and Combining Multiple Choices
• References

Over the last decade, ontology has become an important modeling component in software engineering. Semantic Web Enabled Software Engineering presents some critical findings on opening a new direction of the research of Software Engineering, by exploiting Semantic Web technologies. Most of these findings are from selected papers from the Semantic Web Enabled Software Engineering (SWESE) series of workshops starting from 2005. Edited by two leading researchers, this advanced text presents a unifying and contemporary perspective on the field. The book integrates in one volume a unified perspectiv.

• Data Mining and Machine Learning.- Planning and Combinatorial Optimization.- AI Applications.- Natural Language Processing.- Uncertainty and Preference Reasoning.- Agent Systems.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 30th Canadian Conference on Artificial Intelligence, Canadian AI 2017, held in Edmonton, AB, Canada, in May 2017. The 19 regular papers and 24 short papers presented together with 6 Graduate Student Symposium papers were carefully reviewed and selected from 62 submissions. The focus of the conference was on the following subjects: Data Mining and Machine Learning; Planning and Combinatorial Optimization; AI Applications; Natural Language Processing; Uncertainty and Preference Reasoning; and Agent Systems.
(source: Nielsen Book Data)

• Analysis and Control of a Novel 4-D Hyperchaotic System.- Analysis, Control and Synchronization of a Nine-Term Novel 3-D Chaotic System.- Backstepping Controller Design for the Global Chaos Synchronization of Sprott's Jerk Systems.- Multi-Scroll Chaotic Oscillator Based on a First-Order Delay Differential Equation.- Projective Synchronization Scheme Based on Fuzzy Controller for Uncertain Multivariable Chaotic Systems.- Deadbeat Control for for Multivariable Discrete Time Systems with Time Varying Delay.- Control of Smart Grid Residential Buildings with Demand Response.- Application of Some Modern Techniques in Load Frequency Control in Power Systems.- Investigating Metaheuristics Applications for Capacitated Location Allocation Problem in Logistics Networks.- Classification of Heart Disorders Based on Tunable-Q Wavelet Transform of Cardiac Sound Signals.- Reliability-Constrained Optimal Distribution System Reconfiguration.- Machine Learning aided Efficient Tools for Risk Evaluation and Operational Planning of Multiple Contingencies.- Goal Directed Synthesis of Serial Manipulators Based on Task Descriptions.- Intelligent Tracking Control System for Fast Image Scanning of Atomic Force Microscopes.- Fault Diagnosis Algorithms by Combining Structural Graphs and PCA Approaches for Chemical Processes.
• (source: Nielsen Book Data)

The development of computational intelligence (CI) systems was inspired by observable and imitable aspects of intelligent activity of human being and nature. The essence of the systems based on computational intelligence is to process and interpret data of various nature so that that CI is strictly connected with the increase of available data as well as capabilities of their processing, mutually supportive factors. Developed theories of computational intelligence were quickly applied in many fields of engineering, data analysis, forecasting, biomedicine and others. They are used in images and sounds processing and identifying, signals processing, multidimensional data visualization, steering of objects, analysis of lexicographic data, requesting systems in banking, diagnostic systems, expert systems and many other practical implementations. This book consists of 15 contributed chapters by subject experts who are specialized in the various topics addressed in this book. The special chapters have been brought out in the broad areas of Control Systems, Power Electronics, Computer Science, Information Technology, modeling and engineering applications. Special importance was given to chapters offering practical solutions and novel methods for the recent research problems in the main areas of this book, viz. Control Systems, Modeling, Computer Science, IT and engineering applications. This book will serve as a reference book for graduate students and researchers with a basic knowledge of control theory, computer science and soft-computing techniques. The resulting design procedures are emphasized using Matlab/Simulink software.
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• Consequence
• Logics
• Classical Belief Revision
• AGM Contraction in Non-Classical Logics
• AGM Revision in Logics Without Negation
• Base Revision in Logics Without Negation
• Algorithms for Belief Bases
• Conclusion.

Since the advent of the Semantic Web, interest in the dynamics of ontologies (ontology evolution) has grown significantly. Belief revision presents a good theoretical framework for dealing with this problem; however, classical belief revision is not well suited for logics such as Description Logics. Belief Revision in Non-Classical Logics presents a framework which can be applied to a wide class of logics that include - besides most Description Logics such as the ones behind OWL - Horn Logic and Intuitionistic logic, amongst others. The author also presents algorithms for the most important constructions in belief bases. Researchers and practitioners in theoretical computing will find this an invaluable resource.
(source: Nielsen Book Data)

• Page Rank vs. Katz: Is the centrality algorithm choice relevant to measure user influence in Twitter?.- Weighted means based filters for SAR Imagery.- On Combination of Wavelet Transformation and Stabilized KH Interpolation for Fuzzy Inferences Based on High Dimensional Sampled Functions.- Abductive Reasoning on Molecular Interaction Maps.- Efficient unfolding of fuzzy connectives for multi-adjoint logic programs.- On Generalizations of Concept Lattices.- Generating fuzzy attribute rules via Fuzzy Formal Concept Analysis.
• (source: Nielsen Book Data)

This book presents recent research in the field of interaction between computational intelligence and mathematics, ranging from theory to applications. Computational intelligence, or soft computing consists of various bio-inspired methods, especially fuzzy systems, artificial neural networks, evolutionary and memetic algorithms. These research areas were initiated by professionals in various applied fields, such as engineers, economists, and financial and medical experts. Although computational intelligence offered solutions (at least quasi-optimal solutions) for problems with high complexity, vague and undeterministic features, initially little attention was paid to the mathematical models and analysis of the methods successfully applied. A typical example is the extremely successful Mamdani-algorithm, and its modifications and extensions, applied since the mid-1970s, where the first analysis of the simplest cases, showing why this algorithm was so efficient and stable, was not given until the early 1990s. Since the mid-2000s, the authors have organized international conferences annually to focus on the mathematical methodological issues in connection with computational intelligence approaches. These conferences have attracted a large number of submissions with a wide scope of topics and quality. The editors selected several high-quality papers and approached the authors to submit an essentially extended and improved book chapter based on the lectures.This volume is the first contributed book on the subject.
(source: Nielsen Book Data)

• Preface.-
• Chapter 1: Introduction.- Part I: Generalized Related Set Based Approach.-
• Chapter 2: Information System for Relational Data.-
• Chapter 3: Properties of Granular-Relational Data Mining Framework.-
• Chapter 4: Association Discovery and Classification Rule Mining.-
• Chapter 5: Rough-Granular Computing.- Part II: Description Language Based Approach.-
• Chapter 6: Compound Information Systems.-
• Chapter 7: From Granular-Data Mining Framework to its Relational Version.-
• Chapter 8: Relation-Based Granules.-
• Chapter 9: Compound Approximation Spaces.- Conclusions.- References.- Index.
• (source: Nielsen Book Data)

This book provides two general granular computing approaches to mining relational data, the first of which uses abstract descriptions of relational objects to build their granular representation, while the second extends existing granular data mining solutions to a relational case. Both approaches make it possible to perform and improve popular data mining tasks such as classification, clustering, and association discovery. How can different relational data mining tasks best be unified? How can the construction process of relational patterns be simplified? How can richer knowledge from relational data be discovered? All these questions can be answered in the same way: by mining relational data in the paradigm of granular computing! This book will allow readers with previous experience in the field of relational data mining to discover the many benefits of its granular perspective. In turn, those readers familiar with the paradigm of granular computing will find valuable insights on its application to mining relational data. Lastly, the book offers all readers interested in computational intelligence in the broader sense the opportunity to deepen their understanding of the newly emerging field granular-relational data mining.
(source: Nielsen Book Data)

• Neurocognitive Robot Assistant for Robust Fall Detection .-Smart Robot Control via Novel Computational Intelligence Methods for Ambient Assisted Living
• Valorization of Assistive Technologies for Cognition: Lessons & Practices
• Safe and Automatic Addition of Fault Tolerance for Smart Homes Dedicated to People with Disabilities
• Smart Homes in the Era of Big Data
• An Investigation of The Use of Innovative Biology-based Computational Intelligence in Ubiquitous Robotics Systems: Data Mining Perspective
• Ambient Stupidity
• Security Implementations in Smart Sensor Networks
• Automatic Music Composition from a Self- Learning Algorithm.

This book demonstrates the success of Ambient Intelligence in providing possible solutions for the daily needs of humans. The book addresses implications of ambient intelligence in areas of domestic living, elderly care, robotics, communication, philosophy and others. The objective of this edited volume is to show that Ambient Intelligence is a boon to humanity with conceptual, philosophical, methodical and applicative understanding. The book also aims to schematically demonstrate developments in the direction of augmented sensors, embedded systems and behavioral intelligence towards Ambient Intelligent Networks or Smart Living Technology. It contains chapters in the field of Ambient Intelligent Networks, which received highly positive feedback during the review process. The book contains research work, with in-depth state of the art from augmented sensors, embedded technology and artificial intelligence along with cutting-edge research and development of technologies and applications of Ambient Intelligent Networks. This book is intended to introduce ideas, methods, technologies of the future development of humanity, Science and Technology.

• Intro; Contents; Contributors; Introduction; Part I Search and Optimization; A Metaheuristic for Optimizing the Performance and the Fairness in Job Scheduling Systems; 1 Introduction; 2 Problem Description; 2.1 Machines; 2.2 Jobs; 2.3 Optimization Criteria; 3 Overview of Existing Scheduling Approaches; 3.1 Standard Scheduling Algorithms; 3.2 Scheduling with Job Runtime Estimates; 3.3 User-to-User Fairness and Fair-Share; 3.4 Advanced Optimization Methods; 3.5 Summary; 4 Metaheuristic Scheduler; 4.1 Linear Schedule Compression; 4.2 Schedule Evaluation.

• 4.3 Metaheuristic for Schedule Optimization5 Experiments; 5.1 Simulation Setup; 5.2 Experimental Results; 5.3 Summary; 6 Conclusion and Future Work; References; Hybrid ACO and Tabu Search for Large Scale Information Retrieval; 1 Introduction; 2 Information Retrieval Background; 3 Lex as a Tool for Documents Indexing; 4 AC-IR Algorithm; 4.1 Solutions Encoding; 4.2 Pheromone Table and Probabilistic Decision Rules; 4.3 Updating the Pheromone; 4.4 Building and Improving a Solution; 5 ACS-IR Algorithm; 6 The Overall Algorithm; 7 Experimental Results; 7.1 Benchmarks; 7.2 Setting the Parameters.

• 7.3 Comparison of AS-IR, ACS-IR and CL-IR Algorithms8 Conclusion; References; Hosting Clients in Clustered and Virtualized Environment: A Combinatorial Optimization Approach; 1 Introduction; 1.1 Hardware Virtualization Technology; 1.2 Cluster Computing Technology; 1.3 Clients Hosting Problem; 2 Resource Allocation Problem; 3 Helpful Optimization Problems and Tools; 3.1 2-Dimensional Bin-Packing Problem; 3.2 The Max-Min Problem; 3.3 Data-Set and Solving Tool; 3.4 Branch-and-Bound Search; 4 Proposed Approach; 5 Integer Programming Models; 5.1 Minimizing the Number of Clusters.

• 5.2 Heavy Clients Distribution5.3 Balancing the Use of Resources; 6 Discussions; 7 Conclusion; References; Part II Machine Learning; On the Application of Artificial Intelligence Techniques to Create Network Intelligence; 1 Introduction; 1.1 AI for Internet of Things; 1.2 AI for Telecommunication Networks; 2 Graph Theory for Virus Epidemic Prediction; 2.1 State of the Art; 2.2 Architecture and Implementation; 3 Machine Learning for Smart Building Energy Management; 3.1 State of the Art; 3.2 Architecture and Implementation; 4 Intelligent Middleware for Cloud Robotics; 4.1 State of the Art.

• 4.2 Architecture and Implementation5 Multiple Neural Networks for Client Profiling on Telecommunication Networks; 5.1 State of the Art; 5.2 Architecture and Implementation; 6 Alarm Prediction on Telecommunication Networks; 6.1 State of the Art; 6.2 Architecture and Implementation; 7 Conclusions and Further Applications of AI in ICT; 7.1 Future Work on the Reported Solutions; 7.2 Further Applications of AI on ICT; References; A Statistical Framework for Mental Targets Search Using Mixture Models; 1 Introduction; 2 Related Work; 3 The Framework Structure; 4 Data Model; 5 Update Model.

This book presents various recent applications of Artificial Intelligence in Information and Communication Technologies such as Search and Optimization methods, Machine Learning, Data Representation and Ontologies, and Multi-agent Systems. The main aim of this book is to help Information and Communication Technologies (ICT) practitioners in managing efficiently their platforms using AI tools and methods and to provide them with sufficient Artificial Intelligence background to deal with real-life problems.

• Introduction to the subject of an agent in computer science.- Agent versus decomposition of an algorithm.- M-agent.- The agent system for balancing the distribution of resources.- The examples of applications of the agent systems.- Conclusion. Introduction to the subject of an agent in computer science.- Agent versus decomposition of an algorithm.- M-agent.- The agent system for balancing the distribution of resources.- The examples of applications of the agent systems.- Conclusion.
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This monograph presents the concept of agents and agent systems. It starts with a formal approach and then presents examples of practical applications. In order to form the principles of construction of autonomous agents, a model of the agent is introduced. Subsequent parts of the monograph include several examples of applications of the term agent. Descriptions of different examples of applications of agent systems in such fields as evolution systems, mobile robot systems, artificial intelligence systems are given. The book constitutes an outline of methodology of the design and realization of agent systems based on the M-agent architecture oriented on different areas of applications.
(source: Nielsen Book Data)

• Swarm Intelligence and Evolutionary Computation: Overview and Analysis.- Globally convergent hybridization of particle swarm optimization using line search-based derivative-free techniques Fireflies in the Fruits and Vegetables: Combining the Firefly Algorithm with Goal Programming for Setting Optimal Osmotic Dehydration Parameters of Produce.- Hybrid Metaheuristic Algorithms: Past, Present and Future.- Binary Flower Pollination Algorithm and Its Application to Feature Selection.- Bat Algorithm Application for the Single Row Facility Layout Problem.- Discrete Cuckoo Search Applied to Job Shop Scheduling Problem.- Cuckoo Search and Bat Algorithm Applied to Training Feed-Forward Neural Networks.- The Potential of the Firefly Algorithm for Damage Localization and Stiffness Identification.- Synthesizing Cross-Ambiguity Functions Using An Improved Bat Algorithm.- Sustainable Building Design: A Review on Recent Metaheuristic Methods.- Firefly Algorithm for Flow Shop Optimization.- Evaluation of Harmony Search and Differential Evolution Optimization Algorithms on Solving the Booster Station Optimization Problems in Water Distribution Networks.- Web Document Clustering by Using PSO-Based Cuckoo Search Clustering Algorithm.- Analysis of Trusses Using Particle Swarm Optimization.
• (source: Nielsen Book Data)

This timely review volume summarizes the state-of-the-art developments in nature-inspired algorithms and applications with the emphasis on swarm intelligence and bio-inspired computation. Topics include the analysis and overview of swarm intelligence and evolutionary computation, hybrid metaheuristic algorithms, bat algorithm, discrete cuckoo search, firefly algorithm, particle swarm optimization, and harmony search as well as convergent hybridization. Application case studies have focused on the dehydration of fruits and vegetables by the firefly algorithm and goal programming, feature selection by the binary flower pollination algorithm, job shop scheduling, single row facility layout optimization, training of feed-forward neural networks, damage and stiffness identification, synthesis of cross-ambiguity functions by the bat algorithm, web document clustering, truss analysis, water distribution networks, sustainable building designs and others. As a timely review, this book can serve as an ideal reference for graduates, lecturers, engineers and researchers in computer science, evolutionary computing, artificial intelligence, machine learning, computational intelligence, data mining, engineering optimization and designs.
(source: Nielsen Book Data)

• A Performance Analysis of DF Model in the Energy Harvesting Half-duplex and Full-Duplex Relay Networks
• A Model of Swarm Intelligence Based Optimization Framework Adjustable According to Problems
• Domain Model Definition for Domain-Specific RuleGeneration Using Variability Model
• A set-partitioning-based model to save cost on the import processes
• Combining Genetic Algorithm with Variable Neighborhood Search for MAX-SAT
• Enzyme classification on DUD-E database using Logistic Regression Ensemble (Lorens)
• Consolidation of Host-Based Mobility Management Protocols in Wireless Mesh Network
• Application of parallel computing technologies for numerical simulation of air transport in the human nasal cavity
• Genetic Algorithms-based Techniques for Solving Dynamic Optimization Problems with Unknown Active Variables and Boundaries
• Text Segmentation Methods: A Critical Review
• On-Line Power Systems Security Assessment Using Data Stream Random Forest Algorithm Modification
• Enhanced Security of Internet Banking Authentication with Extended Honey Encryption (XHE) Scheme
• An Enhanced Possibilistic Programming Model with Fuzzy Random Confidence-Interval for Multi-Objective Problem
• A Crowdsourcing Approach for Volunteering System
• One Dimensional Vehicle Tracking Analysis in Vehicular Ad hoc Networks
• Parallel Coordinates Visualization Tool on the Air Pollution Data for Northern Malaysia
• Application of Artificial bee colony algorithm for model parameter identification
• A novel weighting scheme applied to improve the text document clustering techniques
• A Methodological Framework To Emulate The Visual Of Malaysian Shadow Play With Computer-Generated Imagery
• Performance Evaluation of Hot Mix Asphalt Concrete by Using Polymeric Waste Polyethylene.

This book presents the latest research of the field of optimization, modeling and algorithms, discussing the real-world application problems associated with new innovative methodologies. The requirements and demands of problem solving have been increasing exponentially and new computer science and engineering technologies have reduced the scope of data coverage worldwide. The recent advances in information communication technology (ICT) have contributed to reducing the gaps in the coverage of domains around the globe. The book is a valuable reference work for researchers in the fields of computer science and engineering with a particular focus on modeling, simulation and optimization as well as for postgraduates, managers, economists and decision makers.
(source: Nielsen Book Data)

• Bayesian Methods for Intelligent Task Assignment in Crowdsourcing Systems.- Designing Societies of Robots.- On the Origins of Imperfection and Apparent Non-Rationality.- Lasso Granger Causal Models: Some Strategies and their Efficiency for Gene Expression Regulatory Networks.- Cooperative Feature Selection in Personalized Medicine.- Imperfect Decision Making and Risk Taking are affected by Personality.
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This volume focuses on uncovering the fundamental forces underlying dynamic decision making among multiple interacting, imperfect and selfish decision makers. The chapters are written by leading experts from different disciplines, all considering the many sources of imperfection in decision making, and always with an eye to decreasing the myriad discrepancies between theory and real world human decision making. Topics addressed include uncertainty, deliberation cost and the complexity arising from the inherent large computational scale of decision making in these systems. In particular, analyses and experiments are presented which concern: * task allocation to maximize "the wisdom of the crowd"; * design of a society of "edutainment" robots who account for one anothers' emotional states; * recognizing and counteracting seemingly non-rational human decision making; * coping with extreme scale when learning causality in networks; * efficiently incorporating expert knowledge in personalized medicine; * the effects of personality on risky decision making. The volume is a valuable source for researchers, graduate students and practitioners in machine learning, stochastic control, robotics, and economics, among other fields.
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• Cyber Physical Systems Security.-Risk Management for CPS Security.-Wireless Sensor Network Security for Cyber Physical Systems.-WSN Security mechanisms for CPS.- ICS/SCADA System Security for CPS.-Embedded Systems Security for Cyber Physical Systems.-Distributed Control Systems Security for CPS.-Standards for CPS.
• (source: Nielsen Book Data)

This book is a pioneering yet primary general reference resource on cyber physical systems and their security concerns. Providing a fundamental theoretical background, and a clear and comprehensive overview of security issues in the domain of cyber physical systems, it is useful for students in the fields of information technology, computer science, or computer engineering where this topic is a substantial emerging area of study. .
(source: Nielsen Book Data)

• Part I: Main Track.- Long distance in-links for ranking enhancement.- Concept Tracking and Adaptation for Drifting Data Streams under Extreme Verification Latency.- Adversarial Sample Crafting for Time Series Classification with Elastic Similarity Measures.- Slot Co-allocation Optimization in Distributed Computing with Heterogeneous Resources.- About Designing an Observer Pattern-Based Architecture for a Multi-Objective Metaheuristic Optimization Framework.- Scalable Inference of Gene Regulatory Networks with the Spark Distributed Computing Platform.- Finding Best Compiler Options for Critical Software Using Parallel Algorithms.- Drift Detection over Non-stationary Data Streams using Evolving Spiking Neural Networks.- Part II: Energy.- A Hybrid Ensemble of Heterogeneous Regressors for Wind Speed Estimation in Wind Farms.- Bio-inspired approximation to MPPT under real irradiation conditions.- Part III: Industry.- Decision Making in Industry 4.0 Scenarios supported by Imbalanced Data Classification.
• (source: Nielsen Book Data)

This book gathers a wealth of research contributions on recent advances in intelligent and distributed computing, and which present both architectural and algorithmic findings in these fields. A major focus is placed on new techniques and applications for evolutionary computation, swarm intelligence, multi-agent systems, multi-criteria optimization and Deep/Shallow machine learning models, all of which are approached as technological drivers to enable autonomous reasoning and decision-making in complex distributed environments. Part of the book is also devoted to new scheduling and resource allocation methods for distributed computing systems. The book represents the peer-reviewed proceedings of the 12th International Symposium on Intelligent Distributed Computing (IDC 2018), which was held in Bilbao, Spain, from October 15 to 17, 2018.
(source: Nielsen Book Data)

• Modelling of Underwater Robots.- Dynamic Control of 6-DOF AUVs.- Fault Detection/Tolerance Strategies for AUVs and ROVs.- Experiments of Dynamic Control of a 6-DOF AUV.- Kinematic Control of UVMSs.- Dynamic Control of UVMSs.- Interaction Control of UVMSs.- Dynamic Control of 6-DOF AUVs.- Fault Detection/Tolerance Strategies for AUVs and ROVs.- Experiments of Dynamic Control of a 6-DOF AUV.- Kinematic Control of UVMSs.- Dynamic Control of UVMSs.- Interaction Control of UVMSs.
• (source: Nielsen Book Data)

A classic in underwater robotics. One of the first volumes in the "Springer Tracts in Advanced Robotics" series, it has been a bestseller through the previous three editions. Fifteen years after the publication of the first edition, the fourth edition comes to print. The book addresses the main control aspects in underwater manipulation tasks. With respect to the third edition, it has been revised, extended and some concepts better clustered. The mathematical model with significant impact on the control strategy is discussed. The problem of controlling a 6-degrees-of-freedoms autonomous underwater vehicle is investigated and a survey of fault detection/tolerant strategies for unmanned underwater vehicles is provided. Inverse kinematics, dynamic and interaction control for underwater vehicle-manipulator systems are then discussed. The code used to generate most of the numerical simulations is made available and briefly discussed.
(source: Nielsen Book Data)

• Chapter 1 Introduction.-
• Chapter 2 Some Theoretical Background.-
• Chapter 3 Spatial Grasp Model.-
• Chapter 4 SGL Detailed Specification.-
• Chapter 5 Main Spatial Mechanisms in SGL.-
• Chapter 6 SGL Networked Interpreter.-
• Chapter 7 Creation, Activation and Management of a Distributed World.-
• Chapter 8 Parallel and Distributed Network Operations.-
• Chapter 9 Solving Social Problems.-
• Chapter 10 Automated Command and Control.-
• Chapter 11 Collective Robotics.-
• Chapter 12 Conclusions.
• (source: Nielsen Book Data)

The book describes a novel ideology and supporting information technology for integral management of both civil and defence-orientated large, distributed dynamic systems. The approach is based on a high-level Spatial Grasp Language, SGL, expressing solutions in physical, virtual, executive and combined environments in the form of active self-evolving and self-propagating patterns spatially matching the systems to be created, modified and controlled. The communicating interpreters of SGL can be installed in key system points, which may be in large numbers (up to millions and billions) and represent equipped humans, robots, laptops, smartphones, smart sensors, etc. Operating under gestalt-inspired scenarios in SGL initially injected from any points, these systems can be effectively converted into goal-driven spatial machines (rather than computers as dealing with physical matter too) capable of responding to numerous challenges caused by growing world dynamics in the 21st century. Including numerous practical examples, the book is a valuable resource for system managers and programmers.
(source: Nielsen Book Data)

• Aerial Vehicles progress On Pico Air Vehicles.- Perception and Mapping.- Planning.- Systems and Integration.- Control.
• (source: Nielsen Book Data)

This volume presents a collection of papers presented at the 15th International Symposium of Robotic Research (ISRR). ISRR is the biennial meeting of the International Foundation of Robotic Research (IFRR) and its 15th edition took place in Flagstaff, Arizona on December 9 to December 12, 2011. As for the previous symposia, ISRR 2011 followed up on the successful concept of a mixture of invited contributions and open submissions. Therefore approximately half of the 37 contributions were invited contributions from outstanding researchers selected by the IFRR officers and the program committee, and the other half were chosen among the open submissions after peer review. This selection process resulted in a truly excellent technical program which featured some of the very best of robotic research. The program was organized around oral presentation in a single-track format and included for the first time a small number of interactive presentations. The symposium contributions contained in this volume report on a variety of new robotics research results covering a broad spectrum including perception, manipulation, grasping, vehicles and design, navigation, control and integration, estimation and SLAM. .
(source: Nielsen Book Data)

• Introduction
• Foundation of Neuro-Physiology
• Neural Computing
• Mind Model
• Perception
• Visual Information Processing
• Audio Information Processing
• Language
• Learning
• Memory
• Thought
• Development of Intelligence
• Emotion
• Immune System
• Consciousness
• Symbolic Logic
• The Machine Proves
• Perspective.
• (source: Nielsen Book Data)

Intelligence Science is an interdisciplinary subject dedicated to joint research on basic theory and technology of intelligence by brain science, cognitive science, artificial intelligence and others. Brain science explores the essence of brain research on the principle and model of natural intelligence at the molecular, cell and behavior level. Cognitive science studies human mental activity, such as perception, learning, memory, thinking, consciousness etc. In order to implement machine intelligence, artificial intelligence attempts simulation, extension and expansion of human intelligence using artificial methodology and technology. Research scientists from the above three disciplines work together to explore new concepts, new theories, and methodologies.This book will introduce the concept and methodology of intelligence science systematically. The whole book is divided into 18 chapters altogether. It can be regarded as a textbook in courses of intelligence science, cognitive science, cognitive informatics etc. for senior and graduate students. It has important reference value for researchers engaged in fields such as intelligence science, brain science, cognitive science, neural science, artificial intelligence, psychology and so on.
(source: Nielsen Book Data)

• Scott Consequence Relations
• Biconsequence Relations
• Four-Valued Logics
• Nonmonotonic Semantics
• Default Consequence Relations
• Argumentation Theory
• Production and Causal Inference
• Epistemic Consequence Relations
• Modal Nonmonotonic Logics.
• (source: Nielsen Book Data)

Many approaches in the field of nonmonotonic and "commonsense" reasoning are actually different representations of the same basic ideas and constructions. This book gives a logical formalization of the original, explanatory approach to nonmonotonic reasoning. It uses the basic formalism of biconsequence relations, as well as derived systems of default, autoepistemic and causal inference, to cover in a single framework such diverse systems as default logic, autoepistemic and modal nonmonotonic logics, input/output and causal logics, argumentation theory, and semantics of general logic programs with negation as failure. This approach provides a clear separation between logical (monotonic) and nonmonotonic aspects of nonmonotonic reasoning. The separation allows, in particular, to single out the logics underlying modern logic programming and restore thereby the connection between logic programming and logic.
(source: Nielsen Book Data)

• Intro; Title Page; Acknowledgments; Contents; Abstract; Introduction; Federated SPARQL Query Processing; The Need for Efficient Source Selection; The Need for More Comprehensive SPARQL Benchmarks; Contributions; Chapter Overview; Basic Concepts and Notation; Semantic Web; URIs, RDF; SPARQL Query Language; Triplestore; SPARQL Syntax, Semantic and Notation; State of the Art; Federation systems evaluations; Benchmarks; Federated engines public survey; Survey Design; Discussion of the survey results; Details of selected systems; Overview of the selected approaches; Performance Variables.

• EvaluationExperimental setup; Evaluation criteria; Experimental results; Discussion; Effect of the source selection time; Effect of the data partitioning; Hypergraph-Based Source Selection; Problem Statement; HiBISCuS; Queries as Directed Labelled Hypergraphs; Data Summaries; Source Selection Algorithm; Pruning approach; Evaluation; Experimental Setup; Experimental Results; Trie-based Source Selection; TBSS; TBSS Data Summaries; TBSS Source Selection Algorithm; TBSS Pruning approach; QUETSAL; Quetsal's Architecture; Quetsal's SPARQL 1.1 Query Re-writing; Evaluation; Experimental Setup.

• Experimental ResultsDuplicate-Aware Source Selection; DAW; Min-Wise Independent Permutations (MIPs); DAW Index; DAW Federated Query Processing; Experimental Evaluation; Experimental Setup; Experimental Results; Policy-Aware Source Selection; Motivating Scenario; Methodology and Architecture; Evaluation; Experimental Setup; Experimental Results; Data Distribution-Based Source Selection; Motivation; Biological query example; Methods; Transforming TCGA data to RDF; Linking TCGA to the LOD cloud; TCGA data workflow and schema; Data distribution and load balancing.

• TopFed federated query processing approachSource selection; Results and discussion; Evaluation; Availability of supporting data; LargeRDFBench: SPARQL Federation Benchmark; Background; The Need of More Comprehensive SPARQL Federation Benchmark; Benchmark Description; Benchmark Datasets; Benchmark Queries; Performance Metrics; Evaluation; Experimental Setup; SPARQL 1.0 Experimental Results; SPARQL 1.1 Experimental Results; FEASIBLE: SPARQL Benchmarks Generation Framework; Key SPARQL Features; A Comparison of Existing Triple Stores Benchmarks and Query Logs; FEASIBLE Benchmark Generation.

• Data Set CleaningNormalization of Features Vectors; Query Selection; Complexity Analysis; Evaluation and Results; Composite Error Estimation; Experimental Setup; Experimental Results; Conclusion; HiBISCuS; TBSS/Quetsal; DAW; SAFE; TopFed; LargeRDFBench; FEASIBLE; Bibliography.

• History of Artificial Intelligence.- Symbolic Artificial Intelligence.- Computational Intelligence.- Search Methods.- Evolutionary Computing.- Logic-Based Reasoning.- Structural Models of Knowledge Representation.- Syntactic Pattern Analysis.- Rule-Based Systems.- Pattern Recognition and Cluster Analysis.- Neural Networks.- Reasoning with Imperfect Knowledge.- Defining Vague Notions in Knowledge-Based Systems.- Cognitive Architectures.- Theories of Intelligence in Philosophy and Psychology.- Application Areas of AI Systems.- Prospects of Artificial Intelligence.
• (source: Nielsen Book Data)

In the chapters in Part I of this textbook the author introduces the fundamental ideas of artificial intelligence and computational intelligence. In Part II he explains key AI methods such as search, evolutionary computing, logic-based reasoning, knowledge representation, rule-based systems, pattern recognition, neural networks, and cognitive architectures. Finally, in Part III, he expands the context to discuss theories of intelligence in philosophy and psychology, key applications of AI systems, and the likely future of artificial intelligence. A key feature of the author's approach is historical and biographical footnotes, stressing the multidisciplinary character of the field and its pioneers. The book is appropriate for advanced undergraduate and graduate courses in computer science, engineering, and other applied sciences, and the appendices offer short formal, mathematical models and notes to support the reader.
(source: Nielsen Book Data)

• Case-Studies in Mining User-Generated Reviews for Recommendation
• Mining Newsworthy Topics from Social Media
• Sentiment Analysis Using Supervised Learning with Domain-Adaptation and Sentence-Based Analysis
• Pattern-based Emotion Classification on Social Media
• Entity-based Opinion Mining from Text and Multimedia
• Predicting Emotion Labels for Chinese Microblog Texts.

This volume presents a collection of carefully selected contributions in the area of social media analysis. Each chapter opens up a number of research directions that have the potential to be taken on further in this rapidly growing area of research. The chapters are diverse enough to serve a number of directions of research with Sentiment Analysis as the dominant topic in the book. The authors have provided a broad range of research achievements from multimodal sentiment identification to emotion detection in a Chinese microblogging website. The book will be useful to research students, academics and practitioners in the area of social media analysis.

• Advances in Intelligent Systems (Lakhmi C Jain and Kazumi Nakamatsu)
• Stability, Chaos and Limit Cycles in Recurrent Cognitive Reasoning Systems (Aruna Chakraborty, Amit Konar, Pavel Bhowmik and Atulya K Nagar)
• Some Studies on Data Mining (Dilip Kumar Pratihar)
• Rough Non-Deterministic Information Analysis for Uncertain Information (Hiroshi Sakai, Hitomi Okuma, Mao Wu and Michinori Nakata)
• Metamathematical Limits to Computation (N C A da Costa and F A Doria)
• Hypothesis Refinement: Building Hypotheses in an Intelligent Agent System (Gauvain Bourgne, Nicolas Maudet and Suzanne Pinson)
• A Heuristic Algorithmic Procedure to Solve Allocation Problems with Fuzzy Evaluations (R Bartholo, C A N Cosenza, F A Doria and M R Doria)
• Non-Classical Logics and Intelligent Systems (Seiki Akama)
• A Paraconsistent Annotated Logic Program Before-After EVALPSN and Its Application (Kazumi Nakamatsu and Jair Minoro Abe)
• Inspecting and Preferring Abductive Models (Luis Moniz Pereira, Pierangelo Dell'Acqua, Alexandre Miguel Pinto and Goncalo Lopes)
• Supervised Neural Network Learning: From Vectors to Graphs (Monica Bianchini, Marco Maggini and Lorenzo Sarti)
• Paraconsistent Artificial Neural Networks and Applications (Jair Minoro Abe and Kazumi Nakamatsu)
• Paraconsistent Annotated Evidential Logic E and Applications in Automation and Robotics (Jair Minoro Abe and Kazumi Nakamatsu)
• Adaptive Intelligent Learning System for Online Learning Environments (Fatma Cemile Serce, Ferda Nur Alpaslan and Lakhmi C Jain)
• Automatic Test Program Generation: How Artificial Evolution May Outperform Experience (Danilo Ravotto, Ernesto Sanchez and Giovanni Squillero)
• Discovery of Communications Patterns by the Use of Intelligent Reasoning (J Fulcher, M Zhang, Q Bai and F Ren)
• Adaptive Approach to Quality Enhancement and Storage of Signatures and Fingerprint Images (Roumen Kountchev)
• Knowledge Representation for Electronic Circuits in Logic Programming (Takushi Tanaka)
• An Intelligent CBR Model for Predicting Changes in Tropical Cyclones Intensities (James N K Liu, Simon C K Shiu, Jane You and Leon S K Law)
• Analysis of Sequential Data in Tool Manufacturing of Volkswagen AG (Kemal Ince, Thomas Schneider and Frank Klawonn)
• Reasoning-Based Artificial Agents in Agent-Based Computational Economics (Shu-Heng Chen)
• Reasoning and Knowledge Acquisition from Medical Database Using Lattice SOM and Tree Structure SOM (Takumi Ichimura, Takashi Yamaguchi and Kenneth James Mackin)
• Approximate Processing in Medical Diagnosis by Means of Deductive Agents (G Fenza, D Furno, V Loia and S Senatore).
• (source: Nielsen Book Data)

This book consists of various contributions in conjunction with the keywords "reasoning" and "intelligent systems", which widely covers theoretical to practical aspects of intelligent systems. Therefore, it is suitable for researchers or graduate students who want to study intelligent systems generally.
(source: Nielsen Book Data)

• Part I
• Searching and Sorting
• Overview
• 1 Binary Search
• 2 Insertion Sort
• 3 Fast Sorting Algorithms
• 4 Parallel Sorting
• The Need for Speed
• 5 Topological Sorting
• How Should I Begin to Complete My To Do List?
• 6 Searching Texts
• But Fast! The Boyer-Moore-Horspool Algorithm
• 7 Depth-First Search (Ariadne & Co.)
• 8 Pledge's Algorithm
• How to Escape from a Dark Maze
• 9 Cycles in Graphs
• 10 PageRank
• What Is Really Relevant in the World-Wide Web?
• Part II
• Arithmetic and Encryption
• Overview
• 11 Multiplication of Long Integers
• Faster than Long Multiplication
• 12 The Euclidean Algorithm
• 13 The Sieve of Eratosthenes
• How Fast Can We Compute a Prime Number Table?
• 14 One-Way Functions
• Mind the Trap
• Escape Only for the Initiated
• 15 The One-Time Pad Algorithm
• The Simplest and Most Secure Way to Keep Secrets
• 16 Public-Key Cryptography
• 17 How to Share a Secret
• 18 Playing Poker by Email
• 19 Fingerprinting
• 20 Hashing
• 21 Codes
• Protecting Data Against Errors and Loss
• Part III
• Planning, Coordination and Simulation
• Overview
• 22 Broadcasting
• How Can I Quickly Disseminate Information?
• 23 Coverting Numbers into English Words
• 24 Majority
• Who Gets Elected Class Rep?
• 25 Random Numbers
• How Can We Create Randomness in Computers?
• 26 Winning Strategies for a Matchstick Game
• 27 Scheduling of Tournaments or Sports Leagues
• 28 Eulerian Circuits
• 29 High-Speed Circles
• 30 Gauß-Seidel Iterative Method for the Computation of Physical Problems
• 31 Dynamic Programming
• Evolutionary Distance
• Part IV
• Optimisation
• Overview
• 32 Shortest Paths
• 33 Minimum Spanning Trees
• Sometimes Greed Pays Off
• 34 Maximum Flows
• Towards the Stadium During Rush Hour
• 35 Marriage Broker
• 36 The Smallest Enclosing Circle
• A Contribution to Democracy from Switzerland?
• 37 Online Algorithms
• What Is It Worth to Know the Future?
• 38 Bin Packing
• How Do I Get My Stuff into the Boxes
• 39 The Knapsack Problem
• 40 The Travelling Salesman Problem
• 41 Simulated Annealing.

Algorithms specify the way computers process information and how they execute tasks. Many recent technological innovations and achievements rely on algorithmic ideas - they facilitate new applications in science, medicine, production, logistics, traffic, communication and entertainment. Efficient algorithms not only enable your personal computer to execute the newest generation of games with features unimaginable only a few years ago, they are also key to several recent scientific breakthroughs - for example, the sequencing of the human genome would not have been possible without the invention.

• From Interval (Set) and Probabilistic Granules to Set-and-Probabilistic Granules of Higher Order .- Artificial Intelligence Perspectives on Granular Computing
• Calculi of Approximation Spaces in Intelligent Systems .- Feature Discovery through Hierarchies of Rough Fuzzy Sets .- Comparative Study of Fuzzy Information Processing in Type-2 Fuzzy Systems .- Type-2 Fuzzy Similarity on Partial Truth and Intuitionistic Reasoning .- Decision-Making with Second Order Information Granules .- On the Usefulness of Fuzzy Rule Based Systems based on Hierarchical Linguistic Fuzzy Partitions .- Fuzzy Information Granulation with Multiple Levels of Granularity
• A Rough Set Approach to Building Association Rules and Its Applications .- Fuzzy Modeling with Grey Prediction for Designing Power System Stabilizers .- A Weighted Fuzzy Time Series Based Neural Network Approach to Option Price Forecasting .- A Rough Set Approach to Human Resource Development in IT Corporations .- Environmental Applications of Granular Computing and Intelligent Systems.

Information granules are fundamental conceptual entities facilitating perception of complex phenomena and contributing to the enhancement of human centricity in intelligent systems. The formal frameworks of information granules and information granulation comprise fuzzy sets, interval analysis, probability, rough sets, and shadowed sets, to name only a few representatives. Among current developments of Granular Computing, interesting options concern information granules of higher order and of higher type. The higher order information granularity is concerned with an effective formation of information granules over the space being originally constructed by information granules of lower order. This construct is directly associated with the concept of hierarchy of systems composed of successive processing layers characterized by the increasing levels of abstraction. This idea of layered, hierarchical realization of models of complex systems has gained a significant level of visibility in fuzzy modeling with the well-established concept of hierarchical fuzzy models where one strives to achieve a sound tradeoff between accuracy and a level of detail captured by the model and its level of interpretability. Higher type information granules emerge when the information granules themselves cannot be fully characterized in a purely numerical fashion but instead it becomes convenient to exploit their realization in the form of other types of information granules such as type-2 fuzzy sets, interval-valued fuzzy sets, or probabilistic fuzzy sets. Higher order and higher type of information granules constitute the focus of the studies on Granular Computing presented in this study. The book elaborates on sound methodologies of Granular Computing, algorithmic pursuits and an array of diverse applications and case studies in environmental studies, option price forecasting, and power engineering.

• Foundations of Reinforcement Learning
• Abstraction and Knowledge Transfer in Reinforcement Learning
• Qualitative State Space Abstraction
• Generalization and Transfer Learning with Qualitative Spatial Abstraction
• RLPR
• An Aspectualizable State Space Representation
• Empirical Evaluation
• Summary and Outlook.

Reinforcement learning has developed as a successful learning approach for domains that are not fully understood and that are too complex to be described in closed form. However, reinforcement learning does not scale well to large and continuous problems. Furthermore, acquired knowledge specific to the learned task, and transfer of knowledge to new tasks is crucial. In this book, the author investigates whether deficiencies of reinforcement learning can be overcome by suitable abstraction methods. He discusses various forms of spatial abstraction, in particular qualitative abstraction, a form.

An overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier-a limited, but well-established and comprehensively studied model-and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.
(source: Nielsen Book Data)

An overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier-a limited, but well-established and comprehensively studied model-and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.
(source: Nielsen Book Data)

• Processing of Hyperspectral Medical Images: Applications in Dermatology Using Matlab (R).- Foreword.- Preface.- Acknowledgments.- List of selected symbols.- Chapte
• r1: Introduction.- Chapte
• r2: Image acquisition.- Chapte
• r3: Image pre-processing.- Chapte
• r4: Image processing.- Chapte
• r5: Classification.- Chapte
• r6: Sensitivity to parameter changes.- Chapte
• r7: Conclusions.- Appendix.- References.- Summary.
• (source: Nielsen Book Data)

This book presents new methods of analyzing and processing hyperspectral medical images, which can be used in diagnostics, for example for dermatological images. The algorithms proposed are fully automatic and the results obtained are fully reproducible. Their operation was tested on a set of several thousands of hyperspectral images and they were implemented in Matlab. The presented source code can be used without licensing restrictions. This is a valuable resource for computer scientists, bioengineers, doctoral students, and dermatologists interested in contemporary analysis methods.
(source: Nielsen Book Data)

• Locomotion.- Haptics.- Manipulation.- Perception.- Human-Robot Interaction.- Mapping and Localization.- Mechanisms.- Perception and Planning.- Sensor Networks.- Many-Robot Systems.- Interactive Presentations.- Plenary Talk.
• (source: Nielsen Book Data)

The International Symposium on Experimental Robotics (ISER) is a series of bi-annual meetings which are organized in a rotating fashion around North America, Europe and Asia/Oceania. The goal of ISER is to provide a forum for research in robotics that focuses on novelty of theoretical contributions validated by experimental results. The meetings are conceived to bring together, in a small group setting, researchers from around the world who are in the forefront of experimental robotics research. This unique reference presents the latest advances across the various fields of robotics, with ideas that are not only conceived conceptually but also explored experimentally. It collects robotics contributions on the current developments and new directions in the field of experimental robotics, which are based on the papers presented at the 14th ISER held on June 15-18, 2014 in Marrakech and Essaouira, Morocco. This present fourteenth edition of Experimental Robotics edited by M. Ani Hsieh, Oussama Khatib, and Vijay Kumar offers a collection of a broad range of topics in field and human-ce ntered robotics.
(source: Nielsen Book Data)

• Computer Input by Human Eyes Only and it's Applications
• New Ideas for Brain Modelling 2
• Neural-like Growing Networks the Artificial Intelligence Basic Structure
• Detection of Privilege Abuse in RBACAdministered Database
• Learning-based Leaf Image Recognition Frameworks
• Massively Parallel Feature Selection based on Ensemble of Filters and Multiple Robust Consensus Functions for Cancer Gene Identification
• Relationship Discovery and Navigation in Big Graphs
• A Fuzzy System for Three-Factor, Non-textual Authentication
• Efficient Graph-Based Volumetric Segmentation
• A Hybrid Intelligent System in Cultural Intelligence
• Semantic-Based Recommender System with Humain Feeling Relevance Measure
• Alignment of Time Series for Subsequence-to-Subsequence Time Series Matching
• The Effects of Typing Demand on EmotionalStress, Mouse and Keystroke Behaviours
• Friend Recommendation in a Social Bookmarking System: Design and Architecture Guidelines
• Quantum Behaved Genetic Algorithm: Constraints-Handling and GPU Computing
• A Genetic Algorithm Approach for Optimizing a Single-Finger Arabic Keyboard Layout
• Dynamic Well Bottom-Hole Flowing Pressure Prediction Based on Radial Basis Neural Network
• Modeling Energy Consumption in A Educational Building: Comparative Study between Linear Regression, Fuzzy Set Theory and Neural Networks
• Delivering Faster Results Through Parallelisation and GPU Acceleration
• Probabilistic Roadmaps and Hierarchical Genetic Algorithms for Optimal Motion Planning
• Using Mouse and Keyboard Dynamics to Detect Cognitive Stress during Mental Arithmetic
• Towards Using Games Theory to Detect New U2R Attacks
• Development and Evaluation of Virtual Reality Medical Training System for Anatomy Education
• Compression of ECG signal using Hybrid technique
• Performance Analysis of MATLAB Parallel Computing Approaches to Implement Genetic Algorithm for Image Compression.-

The book Intelligent Systems in Science and Information 2014 is ℗ℓthe carefully edited collection of 25 extended chapters from selected papers in the field of Computational Intelligence that, which received highly recommended feedback during the Science and Information Conference (SAI) 2014 review process. All chapters have gone through substantial extension and consolidation and were subject to another round of rigorous review and additional modification and represent the state of the art of the cutting-edge research and technologies in the related areas.

• Preface
• Semi-supervised Learning
• Local Tangent Space Laplacian Eigenmaps
• Multi-Step Model of Switching Reinforcement Learning to Mimic Infants` Motor Development
• Reverse Engineering Networks as Ordinary Differential Equations Systems
• The Relevance of Bayesian Experimental Design for Modeling in Systems Biology
• Topographic Maps for clustering & fast identification of bacteria using 16S housekeeping gene
• Cognitive Modelling of Language Acquisition with Complex Networks
• Process Mining & Visual Analytics: Breathing Life into Business Process Models.
• (source: Nielsen Book Data)

Computational intelligence aims to emulate aspects of biological systems for developing software and/or hardware that learns and adapts. Such systems primarily include neural networks, fuzzy logic systems and evolutionary computation each of them including multiple active and fast growing research directions. This book presents new and ground-breaking research from world-renowned experts focusing on various topics related to computational intelligence. What makes this book particularly interesting is the variety of subjects attained, which offer a global perspective on this central research field in the Knowledge Age.
(source: Nielsen Book Data)

• ADVANCES IN KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS; Frontiers in Artificial Intelligence and Applications; Preface; Contents; Set of Experience and Experiential Decisional DNA; An Estimation of Distribution Algorithm for Solving the Quay Crane Scheduling Problem with Availability Constraints; Performance comparison of non-RNN and RNN in Emergence of Discrete Decision Making through Reinforcement Learning; Genetic Algorithm Solving Orienteering Problem in Large Networks; Optimisation of Ensemble Classifiers using Genetic Algorithm.

• A Learning Based Evolutionary Algorithm For Distributed Multi-Depot VRPPositive Predictive Value based dynamic K-Nearest Neighbor; An Analysis of Clustering Approaches to Distributed Learning on Heterogeneously Distributed Datasets; Unsupervised Discretization Method based on Adjustable Intervals; Evaluation of Random Subspace and Random Forest Regression Models Based on Genetic Fuzzy Syste; A logic for strategies in persuasion dialogue games; Multi-Agent Logic based on Temporary Logic TS4Kn serving Web Search; LMT: A Lightweight Logical Framework for Multi-agent Systems.

• Adaptive organization for cooperative systemsAn optimal tactic for intelligent agents to conduct search & detection operations based on multiple look angles; A complex system approach for a reliable Smart Grid modeling; A Comparison Analysis of Consensus Determining Using One and Two-level Methods; Multi-Agent Logic with Distances, Uncertainty and Interaction Based on Linear Temporal Frames; A framework for handling fuzzy temporal events; On the Continuous Evaluation of the Macrostructure of Sleep.

• Entropic Dimensionality Reduction in Discriminating Between Alzheimer's Disease and Vascular DementiaMeaning Judgment Method for Alphabet Abbreviation Using the Association Mechanism; Method of Constructing the Integral OLAP-model based on Formal Concept Analysis; Semantically Enhanced Text Stemmer (SETS) for Document Clustering; Prosaico: Characterisation of objectives within the scope of an intelligent system for sport advising; Exploiting the Self-Organizing Financial Stability Map; Knowledge-Driven Method for Object Qualification in 3D Point Cloud Data.

• SAC3̂
• A Rule-Based System to Include Context in the Durability Analysis of Civil StructuresPredicting the Final Result of Sporting Events Based on Changes in Bookmaker Odds; A Novel Channel Estimation Scheme Combining Adaptive Supervised and Unsupervised Algorithms; Reference signal cancellation in passive radar using Volterra-Wiener class filter with dynamic structure; Prosodic feature normalization for emotionre cognition by using synthesized speech; Regularity Dimension of Medical Images; Automatic Scoring of Shooting Targets with Tournament Precision.

• Evolutionary computation and its applications. 1. Maximal margin algorithms for pose estimation / Ying Guo and Jiaming Li. 2. Polynomial modeling in a dynamic environment based on a particle swarm optimization / Kit Yan Chan and Tharam S. Dillon. 3. Restoration of half-toned color-quantized images using particle swarm optimization with multi-wavelet mutation / Frank H.F. Leung, Benny C.W. Yeung and Y.H. Chan
• Fuzzy logics and their applications. 4. Hypoglycemia detection for insulin-dependent diabetes mellitus: evolved fuzzy inference system approach / S.H. Ling, P.P. San and H.T. Nguyen
• Neural networks and their applications. 5. Study of limit cycle behavior of weights of perceptron / C.Y.F. Ho and B.W.K. Ling. 6. Artificial neural network modeling with application to nonlinear dynamics / Yi Zhao. 7. Solving Eigen-problems of matrices by neural networks / Yiguang Liu [and others]. 8. Automated screw insertion monitoring using neural networks: a computational intelligence approach to assembly in manufacturing / Bruno Lara, Lakmal D. Seneviratne and Kaspar Althoefer
• Support vector machines and their applications. 9. On the applications of heart disease risk classification and hand-written character recognition using support vector machines / S.R. Alty, H.K. Lam and J. Prada. 10. Nonlinear modeling using support vector machine for heart rate response to exercise / Weidong Chen [and others]. 11. Machine learning-based nonlinear model predictive control for heart rate response to exercise / Yi Zhang [and others]. 12. Intelligent fault detection and isolation of HVAC system based on online support vector machine / Davood Dehestani [and others].

This book focuses on computational intelligence techniques and their applications - fast-growing and promising research topics that have drawn a great deal of attention from researchers over the years. It brings together many different aspects of the current research on intelligence technologies such as neural networks, support vector machines, fuzzy logic and evolutionary computation, and covers a wide range of applications from pattern recognition and system modeling, to intelligent control problems and biomedical applications. Fundamental concepts and essential analysis of various computational techniques are presented to offer a systematic and effective tool for better treatment of different applications, and simulation and experimental results are included to illustrate the design procedure and the effectiveness of the approaches.

• Evolutionary computation and its applications. 1. Maximal margin algorithms for pose estimation / Ying Guo and Jiaming Li. 2. Polynomial modeling in a dynamic environment based on a particle swarm optimization / Kit Yan Chan and Tharam S. Dillon. 3. Restoration of half-toned color-quantized images using particle swarm optimization with multi-wavelet mutation / Frank H.F. Leung, Benny C.W. Yeung and Y.H. Chan
• Fuzzy logics and their applications. 4. Hypoglycemia detection for insulin-dependent diabetes mellitus: evolved fuzzy inference system approach / S.H. Ling, P.P. San and H.T. Nguyen
• Neural networks and their applications. 5. Study of limit cycle behavior of weights of perceptron / C.Y.F. Ho and B.W.K. Ling. 6. Artificial neural network modeling with application to nonlinear dynamics / Yi Zhao. 7. Solving Eigen-problems of matrices by neural networks / Yiguang Liu [and others]. 8. Automated screw insertion monitoring using neural networks: a computational intelligence approach to assembly in manufacturing / Bruno Lara, Lakmal D. Seneviratne and Kaspar Althoefer
• Support vector machines and their applications. 9. On the applications of heart disease risk classification and hand-written character recognition using support vector machines / S.R. Alty, H.K. Lam and J. Prada. 10. Nonlinear modeling using support vector machine for heart rate response to exercise / Weidong Chen [and others]. 11. Machine learning-based nonlinear model predictive control for heart rate response to exercise / Yi Zhang [and others]. 12. Intelligent fault detection and isolation of HVAC system based on online support vector machine / Davood Dehestani [and others].

This book focuses on computational intelligence techniques and their applications - fast-growing and promising research topics that have drawn a great deal of attention from researchers over the years. It brings together many different aspects of the current research on intelligence technologies such as neural networks, support vector machines, fuzzy logic and evolutionary computation, and covers a wide range of applications from pattern recognition and system modeling, to intelligent control problems and biomedical applications. Fundamental concepts and essential analysis of various computational techniques are presented to offer a systematic and effective tool for better treatment of different applications, and simulation and experimental results are included to illustrate the design procedure and the effectiveness of the approaches.

• Machine generated contents note: ch. 1 Introduction
• 1.1. Brief History of AI
• 1.2. Cognitive Issues of AI
• 1.3. Hierarchical Model of Thought
• 1.4. Symbolic Intelligence
• 1.5. Research Approaches of Artificial Intelligence
• 1.6. Automated Reasoning
• 1.7. Machine Learning
• 1.8. Distributed Artificial Intelligence
• 1.9. Artificial Thought Model
• 1.10. Knowledge Based Systems
• Exercises
• ch. 2 Logic Foundation of Artificial Intelligence
• 2.1. Introduction
• 2.2. Logic Programming
• 2.3. Nonmonotonic Logic
• 2.4. Closed World Assumption
• 2.5. Default Logic
• 2.6. Circumscription Logic
• 2.7. Nonmonotonic Logic NML
• 2.8. Autoepistemic Logic
• 2.9. Truth Maintenance System
• 2.10. Situation Calculus
• 2.11. Frame Problem
• 2.12. Dynamic Description Logic
• Exercises
• ch. 3 Constraint Reasoning
• 3.1. Introduction
• 3.2. Backtracking
• 3.3. Constraint Propagation
• 3.4. Constraint Propagation in Tree Search
• 3.5. Intelligent Backtracking and Truth Maintenance.

• 3.6. Variable Instantiation Ordering and Assignment Ordering
• 3.7. Local Revision Search
• 3.8. Graph-based Backjumping
• 3.9. Influence-based Backjumping
• 3.10. Constraint Relation Processing
• 3.11. Constraint Reasoning System COPS
• 3.12. ILOG Solver
• Exercise
• ch. 4 Qualitative Reasoning
• 4.1. Introduction
• 4.2. Basic approaches in qualitative reasoning
• 4.3. Qualitative Model
• 4.4. Qualitative Process
• 4.5. Qualitative Simulation Reasoning
• 4.6. Algebra Approach
• 4.7. Spatial Geometric Qualitative Reasoning
• Exercises
• ch. 5 Case-Based Reasoning
• 5.1. Overview
• 5.2. Basic Notations
• 5.3. Process Model
• 5.4. Case Representation
• 5.5. Case Indexing
• 5.6. Case Retrieval
• 5.7. Similarity Relations in CBR
• 5.8. Case Reuse
• 5.9. Case Retainion
• 5.10. Instance-Based Learning
• 5.11. Forecast System for Central Fishing Ground
• Exercises
• ch. 6 Probabilistic Reasoning
• 6.1. Introduction
• 6.2. Foundation of Bayesian Probability
• 6.3. Bayesian Problem Solving
• 6.4. Naive Bayesian Learning Model.

• 6.5. Construction of Bayesian Network
• 6.6. Bayesian Latent Semantic Model
• 6.7. Semi-supervised Text Mining Algorithms
• Exercises
• ch. 7 Inductive Learning
• 7.1. Introduction
• 7.2. Logic Foundation of Inductive Learning
• 7.3. Inductive Bias
• 7.4. Version Space
• 7.5. AQ Algorithm for Inductive Learning
• 7.6. Constructing Decision Trees
• 7.7. ID3 Learning Algorithm
• 7.8. Bias Shift Based Decision Tree Algorithm
• 7.9. Computational Theories of Inductive Learning
• Exercises
• ch. 8 Support Vector Machine
• 8.1. Statistical Learning Problem
• 8.2. Consistency of Learning Processes
• 8.3. Structural Risk Minimization Inductive Principle
• 8.4. Support Vector Machine
• 8.5. Kernel Function
• Exercises
• ch. 9 Explanation-Based Learning
• 9.1. Introduction
• 9.2. Model for EBL
• 9.3. Explanation-Based Generalization
• 9.4. Explanation Generalization using Global Substitutions
• 9.5. Explanation-Based Specialization
• 9.6. Logic Program of Explanation-Based Generalization
• 9.7. SOAR Based on Memory Chunks.

• 9.8. Operationalization
• 9.9. EBL with imperfect domain theory
• Exercises
• ch. 10 Reinforcement Learning
• 10.1. Introduction
• 10.2. Reinforcement Learning Model
• 10.3. Dynamic Programming
• 10.4. Monte Carlo Methods
• 10.5. Temporal-Difference Learning
• 10.6. Q-Learning
• 10.7. Function Approximation
• 10.8. Reinforcement Learning Applications
• Exercises
• ch. 11 Rough Set
• 11.1. Introduction
• 11.2. Reduction of Knowledge
• 11.3. Decision Logic
• 11.4. Reduction of Decision Tables
• 11.5. Extended Model of Rough Sets
• 11.6. Experimental Systems of Rough Sets
• 11.7. Granular Computing
• 11.8. Future Trends of Rough Set Theory
• Exercises
• ch. 12 Association Rules
• 12.1. Introduction
• 12.2. The Apriori Algorithm
• 12.3. FP-Growth Algorithm
• 12.4. CFP-Tree Algorithm
• 12.5. Mining General Fuzzy Association Rules
• 12.6. Distributed Mining Algorithm For Association Rules
• 12.7. Parallel Mining of Association Rules
• Exercises
• ch. 13 Evolutionary Computation
• 13.1. Introduction
• 13.2. Formal Model of Evolution System Theory.

• 13.3. Darwin's Evolutionary Algorithm
• 13.4. Classifier System
• 13.5. Bucket Brigade Algorithm
• 13.6. Genetic Algorithm
• 13.7. Parallel Genetic Algorithm
• 13.8. Classifier System Boole
• 13.9. Rule Discovery System
• 13.10. Evolutionary Strategy
• 13.11. Evolutionary Programming
• Exercises
• ch. 14 Distributed Intelligence
• 14.1. Introduction
• 14.2. The Essence of Agent
• 14.3. Agent Architecture
• 14.4. Agent Communication Language ACL
• 14.5. Coordination and Cooperation
• 14.6. Mobile Agent
• 14.7. Multi-Agent Environment MAGE
• 14.8. Agent Grid Intelligence Platform
• Exercises
• ch. 15 Artificial Life
• 15.1. Introduction
• 15.2. Exploration of Artificial Life
• 15.3. Artificial Life Model
• 15.4. Research Approach of Artificial Life
• 15.5. Cellular Automata
• 15.6. Morphogenesis Theory
• 15.7. Chaos Theories
• 15.8. Experimental Systems of Artificial Life
• Exercises.

Artificial intelligence is a branch of computer science and a discipline in the study of machine intelligence, that is, developing intelligent machines or intelligent systems imitating, extending and augmenting human intelligence through artificial means and techniques to realize intelligent behavior.Advanced Artificial Intelligence consists of 16 chapters. The content of the book is novel, reflects the research updates in this field, and especially summarizes the author's scientific efforts over many years. The book discusses the methods and key technology from theory, algorithm, system and applications related to artificial intelligence. This book can be regarded as a textbook for senior students or graduate students in the information field and related tertiary specialities. It is also suitable as a reference book for relevant scientific and technical personnel.
(source: Nielsen Book Data)

• Relatedness of Fuzzy Sets, Fuzzification of Allen's Temporal Interval Relation
• Reasoning About Qualitative Relations Between Fuzzy Intervals
• Temporal Information Retrieval with Vague Events
• Representation and Composition of Fuzzy Spatial Relations
• Fuzzification of the Region Connection Calculus
• Reasoning in the Fuzzy Region Connection Calculus
• Geographic Information Retrieval with Vague Regions.
• (source: Nielsen Book Data)

This book comprehensively studies fuzzy temporal and spatial information, starting from the basics on fuzzy set theory and temporal/spatial reasoning, the development of a new model to represent fuzzy temporal/spatial information, the study of efficient and complete reasoning algorithms, and their application in an information retrieval context. This useful volume presents the first approach that goes beyond merely representing information, by thoroughly addressing a variety of reasoning tasks. It also focuses on applications in the domain of information retrieval, and demonstrating the practical importance of the proposed framework.
(source: Nielsen Book Data)

• Introduction and Role of Artificial Neural Networks
• Fundamentals of Biological Neural Networks
• Basic Principles of ANNs and Their Early Structures
• The Perceptron
• The Madaline
• Back Propagation
• Hopfield Networks
• Counter Propagation
• Adaptive Resonance Theory
• The Cognitron and the Neocogntiron
• Statistical Training
• Recurrent (Time Cycling) Back Propagation Networks
• Large Scale Memory Storage and Retrieval (LAMSTAR) Network.
• (source: Nielsen Book Data)

The book should serve as a text for a university graduate course or for an advanced undergraduate course on neural networks in engineering and computer science departments. It should also serve as a self-study course for engineers and computer scientists in the industry. Covering major neural network approaches and architectures with the theories, this text presents detailed case studies for each of the approaches, accompanied with complete computer codes and the corresponding computed results. The case studies are designed to allow easy comparison of network performance to illustrate strengths and weaknesses of the different networks.
(source: Nielsen Book Data)

• Web Intelligence, World Knowledge and Fuzzy Logic.- Towards More Powerful Information Technology via Computing with Words and Perceptions: Precisiated Natural Language, Protoforms and Linguistic Data Summaries.- Enhancing the Power of Search Engines and Navigations Based on Conceptual Model: Web Intelligence.- Soft Computing for Perception-Based Decision Processing and Analysis: Web-Based BISC-DSS.- Evaluating Ontology Based Search Strategies.- Soft Computing for Perception Based Information Processing.- Distributed Architecture for Modeling and Simulation of Autonomous Multi-agent Multi-Physics Systems.- Fuzzy Thesauri for and from the WWW.- Consumer Profiling Using Fuzzy Query and Social Network Techniques.- A Trial to Represent Dynamic Concepts.- SORE (Self Organizable Regulating Engine) - An Example of a Possible Building Block for a "Biologizing" Control System.- Multivariate Non-Linear Feature Selection with Kernel Methods.- A New Fuzzy Spectral Approach to Information Integration in a Search Engine.- Towards Irreducible Modeling of Structures and Functions of Protein Sequences.- Mining Fuzzy Association Rules: An Overview.- A Foundation for Computing with Words: Meta-Linguistic Axioms.- Augmented Fuzzy Cognitive Maps Supplemented with Case Based Reasoning for Advanced Medical Decision Support.- Pruning, Selective Binding and Emergence of Internal Models: Applications to ICA and Analogical Reasoning.- Evolution of the Laws That Deal with the Utilization of Information Networks.- Intelligent Type-2 Fuzzy Inference for Web Information Search Task.- Causality In An Inherently III Defined World.
• (source: Nielsen Book Data)

Search engines, with Google at the top, have become the most heavily used online service, with millions of searches performed every day and many remarkable capabilities. Soft Computing for Information Processing and Analysis includes reports from the front of soft computing in the internet industry and imparts knowledge and understanding of the significance of the field's accomplishments, new developments and future directions. This carefully edited book has evolved from presentations made by the participants of a meeting entitled "Fuzzy Logic and the Internet: Enhancing the Power of the Internet", organized by the Berkeley Initiative in Soft Computing (BISC), University of California, Berkeley. It addresses the important topics of modern search engines such as fuzzy query, decision analysis and support systems, including articles about topics such as Web Intelligence, World Knowledge and Fuzzy Logic (by Lotfi A. Zadeh), perception based information processing, or web intelligence.
(source: Nielsen Book Data)

• Mathematical Tools for Machine Intelligence
• Advanced Applications with Machine Intelligence
• Machine Intelligence for High Level Intelligent Systems.
• (source: Nielsen Book Data)

This book brings together the contributions of leading researchers in the field of machine intelligence, covering areas such as fuzzy logic, neural networks, evolutionary computation and hybrid systems.There is wide coverage of the subject - from simple tools, through industrial applications, to applications in high-level intelligent systems which are biologically motivated, such as humanoid robots (and selected parts of these systems, like the visual cortex). Readers will gain a comprehensive overview of the issues in machine intelligence, a field which promises to play a very important role in the information society of the future.
(source: Nielsen Book Data)

• Mathematical Tools for Machine Intelligence
• Advanced Applications with Machine Intelligence
• Machine Intelligence for High Level Intelligent Systems.
• (source: Nielsen Book Data)

This book brings together the contributions of leading researchers in the field of machine intelligence, covering areas such as fuzzy logic, neural networks, evolutionary computation and hybrid systems.There is wide coverage of the subject - from simple tools, through industrial applications, to applications in high-level intelligent systems which are biologically motivated, such as humanoid robots (and selected parts of these systems, like the visual cortex). Readers will gain a comprehensive overview of the issues in machine intelligence, a field which promises to play a very important role in the information society of the future.
(source: Nielsen Book Data)

For centuries, people have been fascinated by the possibility of building an artificial system that behaves intelligently. Now there is a new entry in this arena - neural networks. Naturally Intelligent Systems offers a comprehensive introduction to these exciting systems. It provides a technically accurate, yet down-to-earth discussion of neural networks, clearly explaining the underlying concepts of key neural network designs, how they are trained, and why they work. Throughout, the authors present actual applications that illustrate neural networks' utility in the new world.
(source: Nielsen Book Data)
Naturally Intelligent Systems offers a comprehensive introduction to neural networks.
(source: Nielsen Book Data)
For centuries, people have been fascinated by the possibility of building an artificial system that behaves intelligently. From Mary Shelley's Frankenstein monster to the computer intelligence of HAL in 2001, scientists have been cast in the role of creator of such devices. Now there is a new entry into this arena, neural networks, and "Naturally Intelligent Systems explores these systems to see how they work and what they can do. Neural networks are not computers in any traditional sense, and they have little in common with earlier approaches to the problem of fabricating intelligent behavior. Instead, they are information processing systems that are physically modeled after the structure of the brain and that are "trained to perform a task rather than programmed like a computer. Neural networks, in fact, provide a tool with problemsolving capabilities - and limitations - strikingly similar to those of animals and people. In particular, they are successful in applications such as speech, vision, robotics, and pattern recognition. "Naturally Intelligent Systems offers a comprehensive introduction to these exciting systems. It provides a technically accurate, yet down-to-earth discussion of neural networks. No particular mathematical background is necessary; it is written for all interested readers. "Naturally Intelligent Systents clearly explains the underlying concepts of key neural network designs, how they are trained, and why they work. It compares their behavior to the natural intelligence found in animals - and people. Throughout, Caudill and Butler bring the field into focus by presenting actual applications that illustrate neural networks' utility in the real world. MaureenCaudill is President of Adaptics, a neural network consulting company in San Diego and author of the popular "Neural Network Primer" articles that appear regularly in "AI Expert. Charles Butler is a Senior Principal Scientist at Physical Sciences in Alexandria, Virginia. He is a specialist in neural network application development. A Bradford Book.
(source: Nielsen Book Data)

• Introduction
• e-Learning so far
• MOOCs, Crowdsourcing and Social Networks
• User Profiling and Personalisation
• Personal Learning Networks, Portfolios and Environments
• Customised e-Learning
• Looking Ahead.

This book reviews a blend of artificial intelligence (AI) approaches that can take e-learning to the next level by adding value through customization. It investigates three methods: crowdsourcing via social networks; user profiling through machine learning techniques, and personal learning portfolios using learning analytics. Technology and education have drawn closer together over the years as they complement each other within the domain of e-learning, and different generations of online education reflect the evolution of new technologies as researcher and developers continuously seek to optimize the electronic medium to enhance the effectiveness of e-learning. Artificial intelligence (AI) for e-learning promises personalized online education through a combination of different intelligent techniques that are grounded in established learning theories while at the same time addressing a number of common e-learning issues. This book is intended for education technologists and e-learning researchers as well as for a general readership interested in the evolution of online education based on techniques like machine learning, crowdsourcing, and learner profiling that can be merged to characterize the future of personalized e-learning.
(source: Nielsen Book Data)

• Motivation.- Closing the Loop: Placing Ontology Usage Analysis in the Ontology Development and Deployment Lifecycle.- Ontology Usage Analysis Framework (OUSAF).- Identification Phase : Ontology Usage Network Analysis Framework (OUN-AF).- Investigation Phase: Empirical Analysis of Domain Ontology Usage (EMP-AF).
• (source: Nielsen Book Data)

This unique book succinctly summarizes the need to measure how ontologies (one of the building blocks of the Semantic Web) are currently being utilized, providing insights for various stakeholders. Where possible it improves and reuses terms in existing vocabularies/ontologies, as recommended by the Linked Data community. Recent advances in the Semantic Web have led to a proliferation of Resource Description Framework (RDF) data, which employ ontologies to semantically describe the information on the Web making it equally understandable for both humans and machines. However, to create a network effect, it is important that selective ontologies are used by more data publishers to improve the value of that ontology. For this to happen, it is vital to discover what is being used from an ontology to semantically annotate the information on the Web specific to a given domain. Answers to such basic but crucial questions can only be achieved by ascertaining how ontologies in the current semantic web are being utilized and adopted. The proposed frameworks to obtain such insights are explained with real-world examples to provide a clear and detailed description of ontology usage analysis. Both theoretical and practical, the book is of value to academics and professionals working in industry. Specifically, it is of primary interest to researchers, graduate students and practitioners in the area of the Semantic Web and its various real-world applications.
(source: Nielsen Book Data)

• Part I Global Robot Path Planning
• 1.
• Introduction to Mobile Robot Path Planning
• 2.
• Background on Artificial Intelligence Algorithms for Global Path Planning
• 3.
• Design and Evaluation of Intelligent Global Path Planning Algorithms 4.
• Integration of Global Path Planners in ROS
• 5.
• Robot Path Planning using Cloud Computing for Large Grid Maps
• Part II Multi-Robot Task Allocation
• 6.
• General Background on Multi-Robot Task Allocation
• 7.
• Different Approaches to Solve the MRTA Problem
• 8.
• Performance Analysis of the MRTA Approaches for Autonomous Mobile Robot .
• (source: Nielsen Book Data)

This book presents extensive research on two main problems in robotics: the path planning problem and the multi-robot task allocation problem. It is the first book to provide a comprehensive solution for using these techniques in large-scale environments containing randomly scattered obstacles. The research conducted resulted in tangible results both in theory and in practice. For path planning, new algorithms for large-scale problems are devised and implemented and integrated into the Robot Operating System (ROS). The book also discusses the parallelism advantage of cloud computing techniques to solve the path planning problem, and, for multi-robot task allocation, it addresses the task assignment problem and the multiple traveling salesman problem for mobile robots applications. In addition, four new algorithms have been devised to investigate the cooperation issues with extensive simulations and comparative performance evaluation. The algorithms are implemented and simulated in MATLAB and Webots.
(source: Nielsen Book Data)

• Database and Graph Theory [16].- Crossover and Permutation.- Similarity Between Graphs in Database by Permutations.- Morphogenetic and Morpheme Network to Structured Worlds.- Formal Description and References in Graph Theory.
• (source: Nielsen Book Data)

This book offers a concise introduction to morphogenetic computing, showing that its use makes global and local relations, defects in crystal non-Euclidean geometry databases with source and sink, genetic algorithms, and neural networks more stable and efficient. It also presents applications to database, language, nanotechnology with defects, biological genetic structure, electrical circuit, and big data structure. In Turing machines, input and output states form a system - when the system is in one state, the input is transformed into output. This computation is always deterministic and without any possible contradiction or defects. In natural computation there are defects and contradictions that have to be solved to give a coherent and effective computation. The new computation generates the morphology of the system that assumes different forms in time. Genetic process is the prototype of the morphogenetic computing. At the Boolean logic truth value, we substitute a set of truth (active sets) values with possible contradictions. The value of a proposition is a set of true and false values. The aim of morphogenetic computing is to use and solve the contradictions in order to transform systems to allow classical computation.
(source: Nielsen Book Data)

• Learning and evolution
• Human interaction
• Robotic simulation.

This book constitutes the refereed proceedings of the First International Symposium on Artificial Life and Intelligent Agents, ALIA 2014, held in Bangor, UK, in November 2014. The 10 revised full papers were carefully reviewed and selected from 20 submissions. The papers are organized in topical sections on learning and evolution; human interaction; robotic simulation.

• Part I. Multi-Robots Systems
• Part II. Data Fusion, Localization and Mapping
• Part III. Security and Dependability
• Part IV. Mobility.

This book compiles some of the latest research in cooperation between robots and sensor networks. Structured in twelve chapters, this book addresses fundamental, theoretical, implementation and experimentation issues. The chapters are organized into four parts namely multi-robots systems, data fusion and localization, security and dependability, and mobility.

• Introduction.- Respondent Privacy.- User Privacy.
• (source: Nielsen Book Data)

This book provides an overview of the research work on data privacy and privacy enhancing technologies carried by the participants of the ARES project. ARES (Advanced Research in Privacy an Security, CSD2007-00004) has been one of the most important research projects funded by the Spanish Government in the fields of computer security and privacy. It is part of the now extinct CONSOLIDER INGENIO 2010 program, a highly competitive program which aimed to advance knowledge and open new research lines among top Spanish research groups. The project started in 2007 and will finish this 2014. Composed by 6 research groups from 6 different institutions, it has gathered an important number of researchers during its lifetime. Among the work produced by the ARES project, one specific work package has been related to privacy. This books gathers works produced by members of the project related to data privacy and privacy enhancing technologies. The presented works not only summarize important research carried in the project but also serve as an overview of the state of the art in current research on data privacy and privacy enhancing technologies.
(source: Nielsen Book Data)

Theory, algorithms, and applications of machine learning techniques to overcome "covariate shift" non-stationarity. As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of-the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non-stationarity.
(source: Nielsen Book Data)

Theory, algorithms, and applications of machine learning techniques to overcome "covariate shift" non-stationarity. As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of-the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non-stationarity.
(source: Nielsen Book Data)

• Preface
• Chapter 1: Understanding and Modifying Data Sources
• Chapter 2: Using Essbase Studio
• Chapter 3: Building the BSO Cube
• Chapter 4: Building the ASO Cube
• Chapter 5: Using EAS for Development
• Chapter 6: Creating Calculation Scripts
• Chapter 7: Using MaxL to Automate Process
• Chapter 8: Data Integration
• Chapter 9: Provisioning Security Using MaxL Editor or Shared Services
• Chapter 10: Developing Dynamic Reports Index.
• (source: Nielsen Book Data)

This cookbook is full of immediately useable recipes showing you the advanced development techniques when building Essbase Applications and how to take these applications further. This cookbook offers practical, task-based, and immediately usable recipes covering a wide range of advanced development techniques to build Essbase Applications and take them further. In addition to its cookbook style, which ensures the solutions are presented in a clear step-by-step manner, its explanations go into great detail, which makes it good learning material for everyone who has experience in Essbase and wants to improve. The book is designed in such a way that you can either read it chapter by chapter or refer to recipes that you want in no particular order. If you are an experienced Essbase developer, Essbase Database Designer or Database Administrator, then this book is for you. This book assumes that you have good knowledge of Oracle Essbase.
(source: Nielsen Book Data)

• Preface
• Special Topics in Pattern Recognition with Applications in Nonprofileration
• Manufacturing Feature Recognition for Mould & Die Designs: Current Status & Future Directions
• Pattern-Recognition Receptors of Oral Epithelia
• Generating-Kernel Based Nonlinear Feature Extraction Methods
• Damage Assessment Based on Pattern Recognition
• Artificial Intelligence Techniques for Assisting the Decision of Making or Postponing the Embryo Transfer
• New Perspectives on a Pattern Recognition Algorithm Based on Haken's Synergetic Computer Network- With a Comment on Artificial Intelligence & Physical Intelligence
• Active Contours for Real Time Applications
• Class Distribution Estimation in Imprecise Domains Based on Supervised Learning
• Quantitative Bioimage Analysis Using Pattern Recognition
• Advances in Mining Emerging Patterns for Supervised Classification
• On the Geometrical Aspect of Biometric Authentication
• Pattern Recognition as a New Method of Numerical Research of the Concrete Dynamic System
• Pattern Recognition from ICA Mixture Modeling
• Index.
• (source: Nielsen Book Data)

Pattern recognition involves tasks such as pattern identification, feature extraction from them and pattern classification in categories, based on the extracted features. This book presents topical research in the study of pattern recognition, including pattern recognition applications in non-proliferation; pattern recognition of machine feature identifications; pattern recognition receptors of oral epithelia; construction damage assessment based on pattern recognition; Haken's pattern recognition algorithm as applied in engineering sciences; active contours for real time applications and quantitative bioimage analysis using pattern recognition.
(source: Nielsen Book Data)

• Multimodal Human Spacecraft Interaction in Remote Environments
• A Framework for Collaborative Aspects of Intelligent Service Robot
• Piecewise Bezier Curves Path Planning with Continuous Curvature Constraint for Autonomous Driving
• Combined Heuristic Approach to Resource-Constrained Project Scheduling Problem
• A Development of Data-Logger for Indoor Environment
• Multiobjective Evolutionary Optimization and Machine Learning: Application to Renewable Energy Predictions
• Hybriding Intelligent Host-Based and Network-Based Stepping Stone Detections
• Open Source Software Use in City Government
• Pheromone-Balance Driven Ant Colony Optimization with Greedy Mechanism
• Study of Pitchfork Bifurcation in Discrete Hopfield Neural Network
• Grammatical Evolution and STE Criterion
• Data Quality in ANFIS Based Soft Sensors
• The Meccano Method for Automatic Volume Parametrization of Solids
• A Buck Converter Model for Multi-Domain Simulations
• The Computer Simulation of Shaping in Rotating Electrical Discharge Machining
• Parameter Identification of a Nonlinear Two Mass System Using Prior Knowledge
• Adaptive and Neural Learning for Biped Robot Actuator Control
• Modeling, Simulation, and Analysis for Battery Electric Vehicles
• Modeling Confined Jets with Particles and Swril
• Robust Tracking and Control of MIMO Processes with Input Saturation and Unknown Disturbance
• Analysis of Priority Rule-Based Scheduling in Dual-Resource-Constrained Shop-Floor Scenarios
• A Hybrid Framework for Servo-Actuated Systems Fault Diagnosis
• Multigrid Finite Volume Method for FGF-2 Transport and Binding
• Integrated Mining Fuzzy Association Rules For Mineral Processing State Identification
• A Combined Cycle Power Plant Simulator: A Powerful, Competitive, and Useful Tool for Operator's Training
• Texture Features Extraction in Mammograms Using Non-Shannon Entropies
• A Wideband DOA Estimation Method Based on Arbitrary Group Delay
• Spatial Speaker Spatial Positioning of Synthesized Speech in Java
• Commercial Break Detection and Content Based Video Retrieval
• ClusterDAM: Clustering Mechanism for Delivery of Adaptive Multimedia Content in Two-Hop Wireless Networks
• Ranking Intervals in Complex Stochastic Boolean Systems Using Intrinsic Ordering
• Predicting Memory Phases
• Information Security Enhancement to Public-Key Cryptosystem Through Magic Squares
• Resource Allocation for Grid Applications: An Economy Model
• A Free and Didactic Implementation of the SEND Protocol for IPv6
• A Survey of Network Benchmark Tools
• Hybrid Stock Investment Strategy Decision Support System
• Towards Performance Analysis of Ad hoc Multimedia Network
• Towards the Performance Optimization of Public-key Algorithms Using Fuzzy Modular Arithematic and Addition Chain
• RBDT-1 Method: Combining Rules and Decision Tree Capabilities
• Computational and Theoretical Concepts for Regulating Stem Cells Using Viral and Physical Methods
• DFA, a Biomedical Checking Tool for the Heart Control System
• Generalizations in Mathematical Epidemiology
• Review of Daily Physical Activity Monitoring System Based on Single Triaxial Accelerometer and Portable Data Measurement Unit
• A Study of the Protein Folding Problem by a Simulation Model
• Analysing Multiobjective Fitness Function with Finite State Automata.

A large international conference on Advances in Machine Learning and Systems Engineering was held in UC Berkeley, California, USA, October 20-22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009). Machine Learning and Systems Engineering contains forty-six revised and extended research articles written by prominent researchers participating in the conference. Topics covered include Expert system, Intelligent decision making, Knowledge-based systems, Knowledge extraction, Data analysis tools, Computational biology, Optimization algorithms, Experiment designs, Complex system identification, Computational modeling, and industrial applications. Machine Learning and Systems Engineering offers the state of the art of tremendous advances in machine learning and systems engineering and also serves as an excellent reference text for researchers and graduate students, working on machine learning and systems engineering.

• What Is Computational Intelligence and Where Is It Going?.- New Millennium AI and the Convergence of History.- The Challenges of Building Computational Cognitive Architectures.- Programming a Parallel Computer: The Ersatz Brain Project.- The Human Brain as a Hierarchical Intelligent Control System.- Artificial Brain and OfficeMate TR based on Brain Information Processing Mechanism.- Natural Intelligence and Artificial Intelligence: Bridging the Gap between Neurons and Neuro-Imaging to Understand Intelligent Behaviour.- Computational Scene Analysis.- Brain-, Gene-, and Quantum Inspired Computational Intelligence: Challenges and Opportunities.- The Science of Pattern Recognition. Achievements and Perspectives.- Towards Comprehensive Foundations of Computational Intelligence.- Knowledge-Based Clustering in Computational Intelligence.- Generalization in Learning from Examples.- A Trend on Regularization and Model Selection in Statistical Learning: A Bayesian Ying Yang Learning Perspective.- Computational Intelligence in Mind Games.- Computer Go: A Grand Challenge to AI.- Noisy Chaotic Neural Networks for Combinatorial Optimization.
• (source: Nielsen Book Data)

In recent years computational intelligence has been extended by adding many other subdisciplines and this new field requires a series of challenging problems that will give it a sense of direction in order to ensure that research efforts are not wasted. This book written by top experts in computational intelligence provides such clear directions and a much-needed focus on the most important and challenging research issues.
(source: Nielsen Book Data)

• Prerequisites.- Simple Learning Classifier Systems.- The XCS Classifier System.- How XCS Works: Ensuring Effective Evolutionary Pressures.- When XCS Works: Towards Computational Complexity.- Effective XCS Search: Building Block Processing.- XCS in Binary Classification Problems.- XCS in Multi-Valued Problems.- XCS in Reinforcement Learning Problems.- Facetwise LCS Design.- Towards Cognitive Learning Classifier Systems.- Summary and Conclusions.
• (source: Nielsen Book Data)

Rule-basedevolutionaryonlinelearningsystems, oftenreferredtoasMichig- style learning classi?er systems (LCSs), were proposed nearly thirty years ago (Holland, 1976; Holland, 1977) originally calling them cognitive systems. LCSs combine the strength of reinforcement learning with the generali- tion capabilities of genetic algorithms promising a ?exible, online general- ing, solely reinforcement dependent learning system. However, despite several initial successful applications of LCSs and their interesting relations with a- mal learning and cognition, understanding of the systems remained somewhat obscured. Questions concerning learning complexity or convergence remained unanswered. Performance in di?erent problem types, problem structures, c- ceptspaces, andhypothesisspacesstayednearlyunpredictable. Thisbookhas the following three major objectives: (1) to establish a facetwise theory - proachforLCSsthatpromotessystemanalysis, understanding, anddesign; (2) to analyze, evaluate, and enhance the XCS classi?er system (Wilson, 1995) by the means of the facetwise approach establishing a fundamental XCS learning theory; (3) to identify both the major advantages of an LCS-based learning approach as well as the most promising potential application areas. Achieving these three objectives leads to a rigorous understanding of LCS functioning that enables the successful application of LCSs to diverse problem types and problem domains. The quantitative analysis of XCS shows that the inter- tive, evolutionary-based online learning mechanism works machine learning competitively yielding a low-order polynomial learning complexity. Moreover, the facetwise analysis approach facilitates the successful design of more - vanced LCSs including Holland's originally envisioned cognitive systems. Martin V.
(source: Nielsen Book Data)

• Scott Consequence Relations
• Biconsequence Relations
• Four-Valued Logics
• Nonmonotonic Semantics
• Default Consequence Relations
• Argumentation Theory
• Production and Causal Inference
• Epistemic Consequence Relations
• Modal Nonmonotonic Logics.
• (source: Nielsen Book Data)

Many approaches in the field of nonmonotonic and "commonsense" reasoning are actually different representations of the same basic ideas and constructions. This book gives a logical formalization of the original, explanatory approach to nonmonotonic reasoning. It uses the basic formalism of biconsequence relations, as well as derived systems of default, autoepistemic and causal inference, to cover in a single framework such diverse systems as default logic, autoepistemic and modal nonmonotonic logics, input/output and causal logics, argumentation theory, and semantics of general logic programs with negation as failure. This approach provides a clear separation between logical (monotonic) and nonmonotonic aspects of nonmonotonic reasoning. The separation allows, in particular, to single out the logics underlying modern logic programming and restore thereby the connection between logic programming and logic.
(source: Nielsen Book Data)

• Paper Sessions Applications 1.- Artificial Intelligence Systems in Micromechanics.- Integrating Two Artificial Intelligence Theories in a Medical Diagnosis Application.- Artificial Intelligence and Law.- Virtual Market Environment for Trade.- Neural Networks and Fuzzy Systems.- An Artificial Neural Networks Approach to the Estimation of Physical Stellar Parameters.- Evolutionary Robot Behaviors Based on Natural Selection and Neural Network.- Control of Overhead Crane by Fuzzy-Pid with Genetic Optimisation.- Creative Design of Fuzzy Logic Controller.- On-Line Extraction of Fuzzy rules in a Wastewater Treatment Plant.- Agents.- An Autonomous Intelligent Agent Architecture Based on Constructivist AI.- Finding Manufacturing Expertise Using Ontologies and Cooperative Agents.- Using Agents in the Exchange of Product Data.- Applications 2.- A Pervasive Identification and Adaptation System for the Smart House.- Deductive Diagnosis of Digital Circuits.- Verification of NASA Emergent Systems.- Theory.- Knowledge Base Structure.- Learning Bayesian Metanetworks from Data with Multilevel Uncertainty.- Using Organizational Structures Emergence for Maintaining Functional Integrity in Embedded Systems Networks.- Efficient Attribute Reduction Algorithm.- Using Relative Logic for Pattern Recognition.- Intelligent Tutoring and Collaboration.- Mathtutor: A Multi-Agent Intelligent Tutoring System.- Analysis and Intelligent Support of Learning Communities in Semi-Structured Discussion Environments.- An Adaptive Assessment System to Evaluate Student Ability Level.- Forming the Optimal Team of Experts for Collaborative Work.- Internet.- Impact on Performance of Hypertext Classification of Selective Rich HTML Capture.- Introducing a Star Topology into Latent Class Models for Collaborative Filtering.- Dialoguing with an Online Assistant in a Financial Domain: The VIP-Advisor Approach.- An Agency for Semantic-Based Automatic Discovery of Web-Services.- Genetic Algorithms.- GESOS: A Multi-Objective Genetic Tool for Project Management Considering Technical and Non-Technical Constraints.- Using Genetic Algorithms and Tabu Search Parallel Models to Solve the Scheduling Problem.- Modelling Document Categories by Evolutionary Learning of Text Centroids.- Ontologies and Data Mining.- ODEval: A Tool for Evaluating RDF(S), DAML+OIL, and OWL Concept Taxonomies.- Air - A Platform for Intelligent Systems.- Swissanalyst.- Data Mining by MOUCLAS Algorithm for Petroleum Reservoir Characterization from Well Logging Data.- Reasoning and Scheduling.- Verification of Procedural Reasoning System (PRS) Programs Using Coloured Petri Nets (CPN).- On-Line Possibilistic Diagnosis Based on Expert Knowledge for Engine Dyno Test Benches.- CBR and Micro-Architecture Anti-Patterns Based Software Design Improvement.- A Decision Support System (DSS) for the Railway Scheduling Problem.- An Interactive Multicriteria Optimisation Approach to Scheduling.
• (source: Nielsen Book Data)

Artificial Intelligence and Innovations (AIAI) will interest researchers, IT professionals and consultants by examining technologies and applications of demonstrable value. The conference focused on profitable intelligent systems and technologies. AIAI focuses on real world applications; therefore authors should highlight the benefits of AI technology for industry and services. Novel approaches solving business and industrial problems, using AI, will emerge from this conference.
(source: Nielsen Book Data)

• Neural network performance metrics
• noise in neural implementations
• simulation requirements and environment
• fault tolerance
• generalisation ability
• learning trajectory and speed.
• (source: Nielsen Book Data)

Hardware inaccuracy and imprecision are important considerations when implementing neural algorithms. This book presents a study of synaptic weight noise as a typical fault model for analogue VLSI realisations of MLP neural networks and examines the implications for learning and network performance. The aim of the book is to present a study of how including an imprecision model into a learning scheme as a"fault tolerance hint" can aid understanding of accuracy and precision requirements for a particular implementation. In addition the study shows how such a scheme can give rise to significant performance enhancement.
(source: Nielsen Book Data)

• Chapter 1. GUI Testing for Introductory Object-Oriented Programming Exercises (Ushio Inoue).-
• Chapter 2. Python Deserialization Denial of Services Attacks and their Mitigations (Kousei Tanaka).-
• Chapter 3. A Branch-and-Bound Based Exact Algorithm for the Maximum Edge-Weight Clique Problem (Satoshi Shimizu).-
• Chapter 4. A Software Model for Precision Agriculture Framework Based on Smart Farming System and Application of IoT Gateway (Symphorien Karl Yoki Donzia).-
• Chapter 5. Components of Mobile Integration in Social Business and E-commerce Application (Mechelle Grace Zaragoza).
• (source: Nielsen Book Data)

• Proposed Framework Application for a Quality Mobile Application Measurement and Evaluation.- Proposal and Development of Artificial Personality (AP) application using the "Requesting" Mechanism.- Load Experiment of the vDACS Scheme in case of the 300 Simultaneous Connection.- Hearing-Dog Robot to wake People up using its Bumping Action.- Implementation of Document Production Support System with Obsession Mechanism.- Detecting Outliners in Terms of Errors in Embedded Software Development Projects Using Imbalance Data Classification.- Development of Congestion State Guiding System for University Cafeteria.- Analog Learning Neural Circuit with Switched Capacitor and the Design of Deep Learning Model.- Study on Category Classification of Conversation Document in Psychological Counseling with Machine Learning.- Improvement of "Multiple Sightseeing Spot Scheduling System".- Advertising in the Webtoon of Cosmetics Brand -Focusing on 'tn' Youth Cosmetics Brands
• Testing Driven Development of Mobile Application using Automatic Bug Management Systems.- Shape Recovery of Polyp from Endoscope Image Using Blood Vessel Information.- Design of Agent Development Framework for RoboCupRescue Simulation.
• (source: Nielsen Book Data)

This book presents the outcomes of the 5th ACIS International Conference on Computational Science/Intelligence & Applied Informatics (CSII 2018), which was held on July 10-12, 2018 in Yonago, Japan. The aim of the conference was to bring together researchers and scientists, businesspeople and entrepreneurs, teachers, engineers, computer users, and students to discuss the various fields of computer science, to share their experiences, and to exchange new ideas and information in a meaningful way. All aspects (theory, applications and tools) of computer and information science, the practical challenges encountered along the way, and the solutions adopted to solve them are all explored here. The conference organizers selected the best papers from among those accepted for presentation. The papers were chosen on the basis of review scores submitted by members of the program committee and subsequently underwent further rigorous review. Following this second round of review, 13 of the conference's most promising papers were selected for this Springer (SCI) book. We eagerly await the important contributions that we know these authors will make to the field of computer and information science.
(source: Nielsen Book Data)
This book presents the scientific outcome of the 4th ACIS International Conference on Computational Science/Intelligence & Applied Informatics (CSII 2017), which was held on July 9-13, 2017 in Hamamatsu, Japan. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science, to share their experiences and to exchange new ideas and information in a meaningful way. The book includes research findings concerning all aspects (theory, applications and tools) of computer and information science, and discusses the practical challenges encountered and the solutions adopted to address them. The book features 16 of the conference's most promising papers, written by researchers who are expected to make significant contributions in the field of computer and information science.
(source: Nielsen Book Data)

• Machine Learning Paradigms: Introduction to Deep Learning-Based Technological Applications
• Part I Deep Learning in Sensing
• Vision to Language: Methods, Metrics and Datasets
• Deep Learning Techniques for Geospatial Data Analysis
• Deep Learning Approaches in Food Recognition
• Part II Deep Learning in Social Media and IOT
• Deep Learning for Twitter Sentiment Analysis: The Effect of Pre-trained Word Embedding
• A Good Defense Is a Strong DNN: Defending the IoT with Deep Neural Networks
• Part III Deep Learning in the Medical Field
• Survey on Deep Learning Techniques for Medical Imaging Application Area
• Deep Learning Methods in Electroencephalography
• Part IV Deep Learning in Systems Control
• The Implementation and the Design of a Hybriddigital PI Control Strategy Based on MISO Adaptive Neural Network Fuzzy Inference System Models-A MIMO Centrifugal Chiller case study
• A Review of Deep Reinforcement Learning Algorithms and Comparative Results on Inverted Pendulum System
• Part V Deep Learning in Feature Vector Processing
• Stock Market Forecasting by Using Support Vector Machines
• An Experimental Exploration of Machine Deep Learning for Drone Conflict Prediction
• Deep Dense Neural Network for Early Prediction of Failure-Prone Students
• Part VI Evaluation of Algorithm Performance
• Non-parametric Performance Measurement with Artificial Neural Networks
• A Comprehensive Survey on the Applications of Swarm Intelligence and Bio-Inspired Evolutionary Strategies
• Detecting Magnetic Field Levels Emitted by Tablet Computers via Clustering Algorithms.

At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in deep learning-based technological applications and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field, (4) Deep Learning in Systems Control, (5) Deep Learning in Feature Vector Processing, and (6) Evaluation of Algorithm Performance. This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.
(source: Nielsen Book Data)

• Part I. Intelligence and decision making system
• A Context-Based Video Compression: A Quantum-Inspired Vector Quantization Approach
• An Enhanced Database Recovery Model Based on Game Theory for Mobile Applications
• Location Estimation of RF Emitting Source Using Supervised Machine Learning Technique
• An Effective Offloading Model Based on Genetic Markov Process for Cloud Mobile Applications
• Toward an Efficient CRWSN Node Based on Stochastic Threshold Spectrum Sensing
• Video Captioning Using Attention Based Visual Fusion with Bi-temporal Context and Bi-modal Semantic Feature Learning
• Matchmoving Previsualization Based on Artificial Marker Detection
• Research Method of Blind Path Recognition Based on DCGAN
• The Impact of the Behavioral Factors on Investment Decision-Making: A Systemic Review on Financial Institutions
• Part II. Deep learning technology and applications (4 papers)
• Part III. Document and sentiment analysis (8 papers)
• Part IV. Blockchain and Cyber Physical System (6 papers)
• Part V Health Informatics and AI Against COVID-19 (7 papers)
• Part VI. Big Data Analytics and Service Quality (4 papers)
• Part VII. Data Mining, Decision Making, and Intelligent Systems (7 papers)
• Part VIII. Power and control systems (10 papers)
• Part IX. Business Intelligence (6 papers)
• Part X. Social media and Digital transformation (5 papers)
• Part XI. Robotic, Control Design and Smart Systems (12 papers).

This book presents the proceedings of the 6th International Conference on Advanced Intelligent Systems and Informatics 2020 (AISI2020), which took place in Cairo, Egypt, from October 19 to 21, 2020. This international and interdisciplinary conference, which highlighted essential research and developments in the fields of informatics and intelligent systems, was organized by the Scientific Research Group in Egypt (SRGE). The book is divided into several sections, covering the following topics: Intelligent Systems, Deep Learning Technology, Document and Sentiment Analysis, Blockchain and Cyber Physical System, Health Informatics and AI against COVID-19, Data Mining, Power and Control Systems, Business Intelligence, Social Media and Digital Transformation, Robotic, Control Design, and Smart Systems.
(source: Nielsen Book Data)

• Possibilistic Framework for Multi-objective Optimization under Uncertainty.- Solving the Uncapacitated Single Allocation p-Hub Median Problem on GPU. Phase Equilibrium Description of a Supercritical Extraction System using Metaheuristic Optimization Algorithms.- Intrusion Detection System based on a behavioral approach.- A new hybrid method to solve the multi-objective optimization problem for a composite hat-stiffened panel.- Storage yard management: modelling and solving.- Multi-capacitated location problem : A new resolution method combining exact and heuristic approaches based on set partitioning.- Application of genetic algorithm for solving bilevel linear programming problems.- Adapted Bin-Packing algorithm for the yard optimization problem.- Hidden Markov Model classifier for the adaptive ACS-TSP pheromone parameters.
• (source: Nielsen Book Data)

This book presents recent research on bioinspired heuristics for optimization. Learning- based and black-box optimization exhibit some properties of intrinsic parallelization, and can be used for various optimizations problems. Featuring the most relevant work presented at the 6th International Conference on Metaheuristics and Nature Inspired Computing, held at Marrakech (Morocco) from 27th to 31st October 2016, the book presents solutions, methods, algorithms, case studies, and software. It is a valuable resource for research academics and industrial practitioners.
(source: Nielsen Book Data)

• Part I Literature Review and State of the Art
• Part II Model for Adaptive Semantics Visualization
• Part III Proof of the Conceptual Model.

This book introduces a novel approach for intelligent visualizations that adapts the different visual variables and data processing to human's behavior and given tasks. Thereby a number of new algorithms and methods are introduced to satisfy the human need of information and knowledge and enable a usable and attractive way of information acquisition. Each method and algorithm is illustrated in a replicable way to enable the reproduction of the entire "SemaVis" system or parts of it. The introduced evaluation is scientifically well-designed and performed with more than enough participants to validate the benefits of the methods. Beside the introduced new approaches and algorithms, readers may find a sophisticated literature review in Information Visualization and Visual Analytics, Semantics and information extraction, and intelligent and adaptive systems. This book is based on an awarded and distinguished doctoral thesis in computer science

• Introduction The Case-Base Reasoning and Decision Making Learning Formal Aspects Summary and Beyond.
• (source: Nielsen Book Data)

Case-based reasoning is a methodology with a long tradition in artificial intelligence that brings together reasoning and machine learning techniques to solve problems based on past experiences or cases. Given a problem to be solved, reasoning involves the use of methods to retrieve similar past cases in order to reuse their solution for the problem at hand. Once the problem has been solved, learning methods can be applied to improve the knowledge based on past experiences. In spite of being a broad methodology applied in industry and services, case-based reasoning has often been forgotten in both artificial intelligence and machine learning books. The aim of this book is to present a concise introduction to case-based reasoning providing the essential building blocks for the design of case-based reasoning systems, as well as to bring together the main research lines in this field to encourage students to solve current CBR challenges.
(source: Nielsen Book Data)

• Foundations of machine learning
• Using AdaBoost to minimize training error
• Direct bounds on the generalization error
• The margins explanation for boosting's effectiveness
• Game theory, online learning, and boosting
• Loss minimization and generalizations of boosting
• Boosting, convex optimization, and information geometry
• Using confidence-rated weak predictions
• Multiclass classification problems
• Learning to rank
• Attaining the best possible accuracy
• Optimally efficient boosting
• Boosting in continuous time.

An accessible introduction and essential reference for an approach to machine learning that creates highly accurate prediction rules by combining many weak and inaccurate ones. Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.
(source: Nielsen Book Data)

• Foundations of machine learning
• Using AdaBoost to minimize training error
• Direct bounds on the generalization error
• The margins explanation for boosting's effectiveness
• Game theory, online learning, and boosting
• Loss minimization and generalizations of boosting
• Boosting, convex optimization, and information geometry
• Using confidence-rated weak predictions
• Multiclass classification problems
• Learning to rank
• Attaining the best possible accuracy
• Optimally efficient boosting
• Boosting in continuous time.

An accessible introduction and essential reference for an approach to machine learning that creates highly accurate prediction rules by combining many weak and inaccurate ones. Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.
(source: Nielsen Book Data)

• Foundations of machine learning
• Using AdaBoost to minimize training error
• Direct bounds on the generalization error
• The margins explanation for boosting's effectiveness
• Game theory, online learning, and boosting
• Loss minimization and generalizations of boosting
• Boosting, convex optimization, and information geometry
• Using confidence-rated weak predictions
• Multiclass classification problems
• Learning to rank
• Attaining the best possible accuracy
• Optimally efficient boosting
• Boosting in continuous time.

Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.
(source: Nielsen Book Data)

• Foundations of machine learning
• Using AdaBoost to minimize training error
• Direct bounds on the generalization error
• The margins explanation for boosting's effectiveness
• Game theory, online learning, and boosting
• Loss minimization and generalizations of boosting
• Boosting, convex optimization, and information geometry
• Using confidence-rated weak predictions
• Multiclass classification problems
• Learning to rank
• Attaining the best possible accuracy
• Optimally efficient boosting
• Boosting in continuous time.

Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.
(source: Nielsen Book Data)

• Preface
• Progressive Organization of Co-Operating Colonies/Collections of Ants/Agents (POOCA) for Competent Pheromone-Based Navigation & Multi-Agent Learning
• Ant Colony Solution to the Optimal Transformer Sizing & Efficiency Problem in Power Systems
• Distributed Decisions: New Insights from Radio-Tagged Ants
• Ant Colony Optimization used in No Wavefront Sensor Adaptive Optics Systems for Solid-State Lasers
• Any Colony Optimization Agents & Path Routing: The Cases of Construction Scheduling & Urban Water Distribution Pipe Networks
• KANTS: A Self-Organized Ant System for Pattern Clustering & Classification
• A Hybrid System Based in Ant Colony & Paraconsistent Logic
• Ant Colony Optimization: A Powerful Strategy for Biomarker Feature Selection
• Any Colony Optimization Based Message Authentication for Wireless Networks
• Index.
• (source: Nielsen Book Data)

Ant colony optimisation is a population-based general-search technique that can be used for the solution of difficult combinatorial problems. Ant colony optimisation uses the knowledge-reinforcing mechanism used by real-life ants while traversing possible paths in search of food, depositing a chemical substance (called pheromone), en-route and subsequently forming trails which can then be followed by other ants in the colony. With its strong mathematical foundation and its simplicity of use, ant colony optimisation provides an alternative method to routing optimisations with a wide range of applications. This book presents topical research in the study of ant colonies, their behaviour and application in computer technologies.
(source: Nielsen Book Data)

• Defeasible Planning through Multi-Agent Argumentation .-Operator behavior modelling in a submarine .-Automatic Wrapper Adaptation by Tree Edit Distance Matching
• Representing Temporal Knowledge in the Semantic Web: The Extended 4D Fluents Approach
• Combining a Multi-Document Update Summarization System -CBSEAS- with a Genetic Algorithm
• Extraction of Essential Events with Application to Damage Evaluation on Fuel Cells
• Detecting car accidents based on traffic flow measurements using machine learning techniques
• Next Generation Environments for Context-aware Learning Design
• Neurules-A Type of Neuro-Symbolic Rules: An Overview.

The combination of different intelligent methods is a very active research area in Artificial Intelligence (AI). The aim is to create integrated or hybrid methods that benefit from each of their components. Some of the existing efforts combine soft computing methods either among themselves or with more traditional AI methods such as logic and rules. Another stream of efforts integrates machine learning with soft-computing or traditional AI methods. Yet another integrates agent-based approaches with logic and also non-symbolic approaches. Some of the combinations have been quite important and more extensively used, like neuro-symbolic methods, neuro-fuzzy methods and methods combining rule-based and case-based reasoning. However, there are other combinations that are still under investigation, such as those related to the Semantic Web. The 2nd Workshop on?Combinations of Intelligent Methods and Applications? (CIMA 2010) was intended to become a forum for exchanging experience and ideas among researchers and practitioners who are dealing with combining intelligent methods either based on first principles or in the context of specific applications. CIMA 2010 was held in conjunction with the 22nd IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2010). Also, a special track was organized in ICTAI 2010, under the same title. This volume includes revised versions of the papers presented in CIMA 2010 and one of the short papers presented in the corresponding ICTAI 2010 special track. It also includes a paper of the editors as invited.

• Theoretical Foundations of Both SI and ANN
• Advances of SI and ANN
• Hybridization of SI and ANN
• Applications.
• (source: Nielsen Book Data)

This book provides a new forum for the dissemination of knowledge in both theoretical and applied research on swarm intelligence (SI) and artificial neural network (ANN). It accelerates interaction between the two bodies of knowledge and fosters a unified development in the next generation of computational model for machine learning.To the best of our knowledge, the integration of SI and ANN is the first attempt to integrate various aspects of both the independent research area into a single volume.
(source: Nielsen Book Data)

• Chapter 1 Introduction 1
• 1.1 Logic, foundations of computer science, and applications of logic to computer science 1
• 1.2 On the utility of logic for computer engineers 3
• Chapter 2 A Few Thoughts Before the Formalization 7
• 2.1 What is logic? 7
• 2.1.1 Logic and paradoxes 8
• 2.1.2 Paradoxes and set theory 9
• l2.1.2.1 The answer 10
• 2.1.3 Paradoxes in arithmetic and set theory 13
• 2.1.3.1 The halting problem 13
• 2.1.4 On formalisms and well-known notions 15
• 2.1.4.1 Some "well-known" notions that could turn out to be difficult to analyze 19
• 2.1.5 Back to the definition of logic 23
• 2.1.5.1 Some definitions of logic for all 24
• 2.1.5.2 A few more technical definitions 24
• 2.1.5.3 Theory and meta-theory (language and meta-language) 30
• 2.1.6 A few thoughts about logic and computer science 30
• 2.2 Some historic landmarks 32
• Chapter 3 Propositional Logic 39
• 3.1 Syntax and semantics 40
• 3.1.1 Language and meta-language 43
• 3.1.2 Transformation rules for cnf and dnf 49
• 3.2 The method of semantic tableaux 54
• 13.2.1 A slightly different formalism: signed tableaux 58
• 3.3 Formal systems 64
• 3.3.1 A capital notion: the notion of proof 64
• 3.3.2 What do we learn from the way we do mathematics? 72
• 3.4 A formal system for PL (PC) 78
• 3.4.1 Some properties of formal systems 84
• 3.4.2 Another formal system for PL (PC) 86
• 3.4.3 Another formal system 86
• 3.5 The method of Davis and Putnam 92
• 3.5.1 The Davis-Putnam method and the SAT problem 95
• 3.6 Semantic trees in PL 96
• 3.7 The resolution method in PL 101
• 3.8 Problems, strategies, and statements 109
• 3.8.1 Strategies 110
• 3.9 Horn clauses 113
• 3.10 Algebraic point of view of propositional logic 114
• Chapter 4 First-order Terms 121
• 4.1 Matching and unification 121
• 4.1.1 A motivation for searching for a matching algorithm 121
• 4.1.2 A classification of trees 123
• 4.2 First-order terms, substitutions, unification 125
• Chapter 5 First-Order Logic (FOL) or Predicate Logic (PL1, PC1) 131
• 5.1 Syntax 133
• 5.2 Semantics 137
• 5.2.1 The notions of truth and satisfaction 139
• 5.2.2 A variant: multi-sorted structures 150
• 5.2.2.1 Expressive power, sort reduction 150
• 5.2.3 Theories and their models 152
• 5.2.3.1 How can we reason in FOL? 153
• 5.3 Semantic tableaux in FOL 154
• 5.4 Unification in the method of semantic tableaux 166
• 5.5 Toward a semi-decision procedure for FOL 169
• 5.5.1 Prenex normal form 169
• 5.5.1.1 Skolemization 174
• 5.5.2 Skolem normal form 176
• 5.6 Semantic trees in FOL 186
• 5.6.1 Skolemization and clausal form 188
• 5.7 The resolution method in FOL 190
• 5.7.1 Variables must be renamed 201
• 5.8 A decidable class: the monadic class 202
• 5.8.1 Some decidable classes 205
• 5.9 Limits: Gödel's (first) incompleteness theorem 206
• Chapter 6 Foundations of Logic Programming 213
• 6.1 Specifications and programming 213
• 6.2 Toward a logic programming language 219
• 6.3 Logic programming: examples 222
• 6.3.1 Acting on the execution control: cut"/" 229
• 6.3.1.1 Translation of imperative structures 231
• 6.3.2 Negation as failure (NAF) 232
• 6.3.2.1 Some remarks about the strategy used by LP and negation as failure 238
• 6.3.2.2 Can we simply deduce instead of using NAF? 239
• 6.4 Computability and Horn clauses 241
• Chapter 7 Artificial Intelligence 245
• 7.1 Intelligent systems: AI 245
• 7.2 What approaches to study AI? 249
• 7.3 Toward an operational definition of intelligence 249
• 7.3.1 The imitation game proposed by Turing 250
• 7.4 Can we identify human intelligence with mechanical intelligence? 251
• 7.4.1 Chinese room argument 252
• 7.5 Some history 254
• 7.5.1 Prehistory 254
• 7.5.2 History 255
• 7.6 Some undisputed themes in AI 256
• Chapter 8 Inference 259
• 8.1 Deductive inference 260
• 8.2 An important concept: clause subsumption 266
• 8.2.1 An important problem 268
• 8.3 Abduction 273
• 8.3.1 Discovery of explanatory theories 274
• 8.3.1.1 Required conditions 275
• 8.4 Inductive inference 278
• 8.4.1 Deductive inference 279
• 8.4.2 Inductive inference 280
• 8.4.3 Hempel's paradox (1945) 280
• 8.5 Generalization: the generation of inductive hypotheses 284
• 8.5.1 Generalization from examples and counter examples 288
• Chapter 9 Problem Specification in Logical Languages 291
• 9.1 Equality 291
• 9.1.1 When is it used? 292
• 9.1.2 Some questions about equality 292
• 9.1.3 Why is equality needed? 293
• 9.1.4 Whatis equality? 293
• 9.1.5 How to reason with equality? 295
• 9.1.6 Specification without equality 296
• 9.1.7 Axiomatization of equality 297
• 9.1.8 Adding the definition of = and using the resolution method 297
• 9.1.9 By adding specialized rules to the method of semantic tableaux 299
• 9.1.10 By adding specialized rules to resolution 300
• 9.1.10.1 Paramodulation and demodulation 300
• 9.2 Constraints 309
• 9.3 Second Order Logic (SOL): a few notions 319
• 9.3.1 Syntax and semantics 324
• 9.3.1.1 Vocabulary 324
• 9.3.1.2 Syntax 325
• 9.3.1.3 Semantics 325
• Chapter 10 Non-classical Logics 327
• l0.l Many-valued logics 327
• 10.1.1 How to reason with p-valued logics? 334
• 10.1.1.1 Semantic tableaux for p-valued logics 334
• 10.2 Inaccurate concepts: fuzzy logic 337
• 10.2.1 Inference in FL 348
• 10.2.1.1 Syntax 349
• 10.2.1.2 Semantics 349
• 10.2.2 Herbrand's method in FL 350
• 10.2.2.1 Resolution andFL 351
• 10.3 Modal logics 353
• 10.3.1 Toward a semantics 355
• 10.3.1.1 Syntax (language of modal logic) 357
• 10.3.1.2 Semantics 358
• 10.3.2 How to reason with modallogics? 360
• 10.3.2.1 Formal systems approach 360
• 10.3.2.2 Translation approach 361
• 10.4 Some elements of temporal logic 371
• 10.4.1 Temporal operators and semantics 374
• 10.4.1.1 A famous argument 375
• 10.4.2 A temporal logic 377
• 10.4.3 How to reason with temporal logics? 378
• 10.4.3.1 The method of semantic tableaux 379
• 10.4.4 An example of a PL for linear and discrete time; PTL (or PLTL) 381
• 10.4.4.1 Syntax 331
• 10.4.4.2 Semantics 382
• 10.4.4.3 Method of semantic tableaux for PLTL (direct method) 333
• Chapter 11 Knowledge and Logic: Some Notions 385
• 11.1 What is knowledge? 335
• 11.2 Knowledge and modal logic 389
• 11.2.1 Toward a formalization 389
• 11.2.2 Syntax 339
• 11.2.2.1 What expressive power? An example 389
• 11.2.2.2 Semantics 339
• 11.2.3 New modal operators 391
• 11.2.3.1 Syntax (extension) 391
• 11.2.3.2 Semantics (extension) 391
• 11.2.4 Application examples 392
• 11.2.4.1 Modeling the muddy children puzzle 392
• 11.2.4.2 Corresponding Kripke worlds 392
• 11.2.4.3 Properties of the (formalization chosen for the) knowledge 394
• Chapter 12 Solutions to the Exercises 395.

Logic and its components (propositional, first-order, non-classical) play a key role in Computer Science and Artificial Intelligence. While a large amount of information exists scattered throughout various media (books, journal articles, webpages, etc.), the diffuse nature of these sources is problematic and logic as a topic benefits from a unified approach. Logic for Computer Science and Artificial Intelligence utilizes this format, surveying the tableaux, resolution, Davis and Putnam methods, logic programming, as well as for example unification and subsumption. For non-classical logics, the translation method is detailed. Logic for Computer Science and Artificial Intelligence is the classroom-tested result of several years of teaching at Grenoble INP (Ensimag). It is conceived to allow self-instruction for a beginner with basic knowledge in Mathematics and Computer Science, but is also highly suitable for use in traditional courses. The reader is guided by clearly motivated concepts, introductions, historical remarks, side notes concerning connections with other disciplines, and numerous exercises, complete with detailed solutions, The title provides the reader with the tools needed to arrive naturally at practical implementations of the concepts and techniques discussed, allowing for the design of algorithms to solve problems.
(source: Nielsen Book Data)

• Information Theory, Machine Learning, and Reproducing Kernel Hilbert Spaces.- Renyi's Entropy, Divergence and Their Nonparametric Estimators.- Adaptive Information Filtering with Error Entropy and Error Correntropy Criteria.- Algorithms for Entropy and Correntropy Adaptation with Applications to Linear Systems.- Nonlinear Adaptive Filtering with MEE, MCC, and Applications.- Classification with EEC, Divergence Measures, and Error Bounds.- Clustering with ITL Principles.- Self-Organizing ITL Principles for Unsupervised Learning.- A Reproducing Kernel Hilbert Space Framework for ITL.- Correntropy for Random Variables: Properties and Applications in Statistical Inference.- Correntropy for Random Processes: Properties and Applications in Signal Processing.
• (source: Nielsen Book Data)

This bookisan outgrowthoften yearsof researchatthe Universityof Florida Computational NeuroEngineering Laboratory (CNEL) in the general area of statistical signal processing and machine learning. One of the goals of writing the book is exactly to bridge the two ?elds that share so many common problems and techniques but are not yet e?ectively collaborating. Unlikeotherbooks thatcoverthe state ofthe artinagiven?eld, this book cuts across engineering (signal processing) and statistics (machine learning) withacommontheme:learningseenfromthepointofviewofinformationt- orywithanemphasisonRenyi'sde?nitionofinformation.Thebasicapproac h is to utilize the information theory descriptors of entropy and divergence as nonparametric cost functions for the design of adaptive systems in unsup- vised or supervised training modes. Hence the title: Information-Theoretic Learning (ITL). In the course of these studies, we discovered that the main idea enabling a synergistic view as well as algorithmic implementations, does not involve the conventional central moments of the data (mean and covariance). Rather, the core concept is the ?-norm of the PDF, in part- ular its expected value (? = 2), which we call the information potential. This operator and related nonparametric estimators link information theory, optimization of adaptive systems, and reproducing kernel Hilbert spaces in a simple and unconventional way.
(source: Nielsen Book Data)

• Investigating Universal Computability of Conventional Cellular Automata Problems on an Organic Molecular Matrix Noise-Based Logic and Computing: From Boolean Logic Gates to Brain Circuitry and Its Possible Hardware Realization Models and Mechanisms for Artificial Morphogenesis Biologically-Inspired Network Architecture for Future Networks Foraging Behaviors and Potential Computational Ability of Problem-Solving in an Amoeba Two Molecular Information Processing Systems Based on Catalytic Nucleic Acids The Effect of Community on Distributed Bio-inspired Service Composition Efficient Computation in Brownian Cellular Automata A Molecular Communication System Properties of Threshold Coupled Chaotic Neuronal Maps Implementation of Rotary Element with Quantum Cellular Automata Universal 2-State Asynchronous Cellular Automaton with Inner-Independent Transitions Effect of Population Size in Extended Parameter-Free Genetic Algorithm Temperature Effects on Olive Fruit Fly Infestation in the FlySim Cellular Automata Model Computing by Observing Changes Robustness of the Critical Behaviour in a Discrete Stochastic Reaction-Diffusion Medium Quantifying the Severity of the Permutation Problem in Neuroevolution Extending the Geometrical Design of DNA Nanostructures An Optical Solution for the Subset Sum Problem Design of True Random One-Time Pads in DNA XOR Cryptosystem On Designing Gliders in Three-Dimensional Larger than Life Cellular Automata Instability of Collective Flow in Two-Dimensional Optimal Velocity Model A New Differential Evolution for Multiobjective Optimization by Uniform Design and Minimum Reduce Hypervolume Noise Effects on Chaos in Chaotic Neuron Model Application of Improved Grammatical Evolution to Santa Fe Trail Problems Limit Theorem for a Time-Dependent Coined Quantum Walk onthe Line Top-Predator Survivor Region Is Affected by Bottom-Prey Mortality Rate on the Monte-Carlo Simulation in Lattice Model Simulation and Theoretical Comparison between 'Zipper' and 'Non-Zipper' Merging Universality of 2-State 3-Symbol Reversible Logic Elements - A Direct Simulation Method of a Rotary Element Pump Current as a Signal Transformation Evaluation of Generation Alternation Models in Evolutionary Robotics Photonic Switching of DNA's Position That Represents the Internal State in Photonic DNA Automaton Fluctuation Induced Structure in Chemical Reaction with Small Number of Molecules Parallel Retrieval of Nanometer-Scale Light-Matter Interactions for Nanophotonic Systems A Compressible Fluid Model for Traffic Flow and Nonlinear Saturation of Perturbation Growth Functional Sized Population Magnetic Optimization Algorithm Emergence and Collapse of Order in Ad Hoc Cellular Automata A Transition Rule Set for the First 2-D Optimum-Time Synchronization Algorithm A Two-Dimensional Optimum-Time Firing Squad Synchronization Algorithm and Its Implementation Quaternion Based Thermal Condition Monitoring System Firing Correlation in Spiking Neurons with Watts-Strogatz Rewiring Methods for Shortening Waiting Time in Walking-Distance Introduced Queueing Systems Effect of Mutation to Distribution of Optimum Solution in Genetic Algorithm.
• (source: Nielsen Book Data)

This book is the refereed proceedings of the Fourth International Workshop on Natural Computing, IWNC 2009, held in Himeji International Exchange Center, HIMEJI, JAPAN on September 2009. IWNC aims to bring together computer scientists, biologists, mathematicians, electronic engineers, physicists, and humanitarians, to critically assess present findings in the field, and to outline future developments in nature-inspired computing.
(source: Nielsen Book Data)

• Part 1 Keynote Speakers
• Part 2 Quantitative Logic
• Part 3 Fuzzy Sets and Systems
• Part 4 Soft Computing.

This book is the proceedings of the 2nd International Conference on Quantitative Logic and Soft Computing (QL & SC 2010) from Oct. 22-25, 2010 in Xiamen, China. Only high-quality papers are included. The 2nd International Conference on Quantitative Logic and Soft Computing (QL & SC 2010), built on the success of previous conferences, the QL & QS 2009 (Shanghai, China), is a major symposium for scientists, engineers and practitioners in China to present their updated results, ideas, developments and applications in all areas of quantitative logic and soft computing. It aims to strengthen relations between industry research laboratories and universities, and to create a primary symposium for world scientists in quantitative logic and soft computing fields as follows: 1) Quantitative Logic. 2) Fuzzy Sets and Systems. 3) Soft Computing.

• 1. Programming-by-Demonstration of Robot Motions
• 2. Grasp Recognition by Fuzzy Modeling and Hidden Markov Models
• 3. Distributed Adaptive Coordinated Control of Multi-Manipulator Systems Using Neural Networks
• 4. A New Framework for View-invariant Human Action Recognition
• 5. Using Fuzzy Gaussian Inference and Genetic Programming to Classify 3D Human Motions
• 6. Obstacle Detection using Cross-ratio and Disparity Velocity
• 7. Learning and Vision-based Obstacle Avoidance and Navigation
• 8. A Fraction Distortion Model for Accurate Camera Calibration and Correction
• 9. A Leader-follower Flocking System Based on Estimated Flocking Center
• 10. A Behavior Based Control System for Surveillance
• 11. Hierarchical Composite Anti-Disturbance Control for Robotic Systems Using Robust Disturbance Observer
• 12. Autonomous Navigation for Mobile Robots with Human-Robot Interaction
• 13. Prediction-based Perceptual System of a Partner Robot for Natural Communication
• Index.

Robot Intelligence is an exciting interdisciplinary field including engineering, information technology, machine learning, biological science and psychology. Its dramatic growth in practical applications is driven by both real-world requirements and maturity of related disciplines such as intelligent algorithms. It is expected that perception, understanding and reasoning capabilities play a crucial role in robot-assisted tasks and enable robots to exhibit similar performance on executing various tasks in both constrained and unconstrained environments. Robot Intelligence is a rare collection of chapters reflecting recent robotics developments from the advanced knowledge processing perspective. It also provides a comprehensive introduction and methodology to selected robotics topics including human robot interaction, human motion analysis, robot vision and advanced control. The robot intelligence methods presented enable readers to address many complex problems involving a wide range of robot sensors for the perception and understanding of the environment and the reasoning of the subsequent actions. The book's comprehensive coverage will prove invaluable to senior undergraduates, PhD students, researchers and practitioners.

• Introduction to Approaches in the Development of Heterogeneous Catalysts
• Methods of Searching for Optimal Catalytic Materials
• Analysis and Mining of Data Gathered in Catalytic Experiments
• Artificial Neural Networks in the Study of Catalytic Performance.
• (source: Nielsen Book Data)

The book provides a comprehensive treatment of combinatorial development of heterogeneous catalysts. In particular, two computer-aided approaches that have played a key role in combinatorial catalysis and high-throughput experimentation during the last decade - evolutionary optimization and artificial neural networks - are described. The book is unique in that it describes evolutionary optimization in a broader context of methods of searching for optimal catalytic materials, including statistical design of experiments, as well as presents neural networks in a broader context of data analysis. It is the first book that demystifies the attractiveness of artificial neural networks, explaining its rational fundamental - their universal approximation capability. At the same time, it shows the limitations of that capability and describes two methods for how it can be improved. The book is also the first that presents two other important topics pertaining to evolutionary optimization and artificial neural networks: automatic generating of problem-tailored genetic algorithms, and tuning evolutionary algorithms with neural networks. Both are not only theoretically explained, but also well illustrated through detailed case studies.
(source: Nielsen Book Data)

• Introduction to Approaches in the Development of Heterogeneous Catalysts
• Methods of Searching for Optimal Catalytic Materials
• Analysis and Mining of Data Gathered in Catalytic Experiments
• Artificial Neural Networks in the Study of Catalytic Performance.
• (source: Nielsen Book Data)

The book provides a comprehensive treatment of combinatorial development of heterogeneous catalysts. In particular, two computer-aided approaches that have played a key role in combinatorial catalysis and high-throughput experimentation during the last decade - evolutionary optimization and artificial neural networks - are described. The book is unique in that it describes evolutionary optimization in a broader context of methods of searching for optimal catalytic materials, including statistical design of experiments, as well as presents neural networks in a broader context of data analysis. It is the first book that demystifies the attractiveness of artificial neural networks, explaining its rational fundamental - their universal approximation capability. At the same time, it shows the limitations of that capability and describes two methods for how it can be improved. The book is also the first that presents two other important topics pertaining to evolutionary optimization and artificial neural networks: automatic generating of problem-tailored genetic algorithms, and tuning evolutionary algorithms with neural networks. Both are not only theoretically explained, but also well illustrated through detailed case studies.
(source: Nielsen Book Data)

• Humanoid Robotics.- Time-Varying Affective Response for Humanoid Robots.- The Co-simulation of Humanoid Robot Based on Solidworks, ADAMS and Simulink.- From RoboNova to HUBO: Platforms for Robot Dance.- BunnyBot: Humanoid Platform for Research and Teaching.- Teen Sized Humanoid Robot: Archie.- Interdisciplinary Construction and Implementation of a Human Sized Humanoid Robot by Master Students.- Human Robot Interaction.- Safety Aspects in a Human-Robot Interaction Scenario: A Human Worker Is Co-operating with an Industrial Robot.- Integration of a RFID System in a Social Robot.- A Practical Study on the Design of a User-Interface Robot Application.- Infrared Remote Control with a Social Robot.- BlogRobot: Mobile Terminal for Blog Browse Using Physical Representation.- An Exploratory Investigation into the Effects of Adaptation in Child-Robot Interaction.- Devious Chatbots - Interactive Malware with a Plot.- Towards Better Human Robot Interaction: Understand Human Computer Interaction in Social Gaming Using a Video-Enhanced Diary Method.- Promotion of Efficient Cooperation by Sharing Environment with an Agent Having a Body in Real World.- Interaction Design for a Pet-Like Remote Control.- Experiences with a Barista Robot, FusionBot.- Mutually Augmented Cognition.- How Humans Optimize Their Interaction with the Environment: The Impact of Action Context on Human Perception.- Development of a Virtual Presence Sharing System Using a Telework Chair.- PLEXIL-DL: Language and Runtime for Context-Aware Robot Behaviour.- Ambient Intelligence in a Smart Home for Energy Efficiency and Eldercare.- Education and Entertainment.- Intelligent Technologies for Edutainment Using Multiple Robots.- Remote Education Based on Robot Edutainment.- Not Just "Teaching Robotics" but "Teaching through Robotics".- A Proposal of Autonomous Robotic Systems Educative Environment.- Mechatronics Education: From Paper Design to Product Prototype Using LEGO NXT Parts.- Fostering Development of Students' Collective and Self-efficacy in Robotics Projects.- From an Idea to a Working Robot Prototype: Distributing Knowledge of Robotics through Science Museum Workshops.- Teaching Electronics through Constructing Sensors and Operating Robots.- Learning from Analogies between Robotic World and Natural Phenomena.- Integrating Robot Design Competitions into the Curriculum and K-12 Outreach Activities.- Teamwork and Robot Competitions in the Undergraduate Program at the Copenhagen University College of Engineering.- Cooperative Robotics.- Multiagents System with Dynamic Box Change for MiroSot.- Multi Block Localization of Multiple Robots.- Soty-Segment: Robust Color Patch Design to Lighting Condition Variation.- Task-Based Flocking Algorithm for Mobile Robot Cooperation.- Analysis of Spatially Limited Local Communication for Multi-Robot Foraging.- AMiRESot - A New Robot Soccer League with Autonomous Miniature Robots.- Robotic System Design.- BeBot: A Modular Mobile Miniature Robot Platform Supporting Hardware Reconfiguration and Multi-standard Communication.- System Design for Semi-automatic AndroSot.- Learning, Optimization, Communication.- Extended TA Algorithm for Adapting a Situation Ontology.- An Integer-Coded Chaotic Particle Swarm Optimization for Traveling Salesman Problem.- USAR Robot Communication Using ZigBee Technology.
• (source: Nielsen Book Data)

th This volume is an edition of the papers selected from the 12 FIRA RoboWorld C- gress, held in Incheon, Korea, August 16-18, 2009. The Federation of International Robosoccer Association (FIRA - www. fira. net) is a non-profit organization, which organizes robotic competitions and meetings around the globe annually. The RoboSoccer competitions started in 1996 and FIRA was - tablished on June 5, 1997. The Robot Soccer competitions are aimed at promoting the spirit of science and technology to the younger generation. The congress is a forum in which to share ideas and future directions of technologies, and to enlarge the human networks in robotics area. The objectives of the FIRA Cup and Congress are to explore the technical dev- opment and achievement in the field of robotics, and provide participants with a robot festival including technical presentations, robot soccer competitions and exhibits - der the theme "Where Theory and Practice Meet. " th Under the umbrella of the 12 FIRA RoboWorld Incheon Congress 2009, six int- national conferences were held for greater impact and scientific exchange: th * 6 International Conference on Computational Intelligence, Robotics and Autonomous Systems (CIRAS) th * 5 International Symposium on Autonomous Minirobots for Research and Edutainment (AMiRE) * International Conference on Social Robotics (ICSR) * International Conference on Advanced Humanoid Robotics Research (ICAHRR) * International Conference on Entertainment Robotics (ICER) * International Robotics Education Forum (IREF) This volume consists of selected quality papers from the six conferences.
(source: Nielsen Book Data)

• A Review of Search and Resource Discovery Techniques in Peer-to-Peer Networks (S Botros & S Waterhouse)
• Adaptive Content Mapping for Internet Navigation (R W Brause & M Ueberall)
• Flexible Queries to XML Information (E Damiani et al.)
• Agent-Based Hypermedia Models (W Balzano et al.)
• Self-Organizing Neural Networks Application for Information Organization (R Rizzo)
• Emotion-Orientated Intelligent Systems (T Ichimura et al.)
• Public Opinion Channel: A Network-Based Interactive Broadcasting System for Supporting a Knowledge-Creating Community (T Fukuhara et al.)
• A New Era of Intelligent E-Commerce Based on Intelligent Java Agent-Based Development Environment (iJADE) (R S T Lee)
• Automated Internet Trading Based on Optimized Physics Models of Markets (L Ingber & R P Mondescu)
• Implementing and Maintaining a Web Case-Based Reasoning System for Heating Ventilation and Air Conditioning Systems Sales Support (I Watson).
• (source: Nielsen Book Data)

The Internet/WWW has made it possible to easily access quantities of information never available before. However, both the amount of information and the variation in quality pose obstacles to the efficient use of the medium. Artificial intelligence techniques can be useful tools in this context. Intelligent systems can be applied to searching the Internet and data-mining, interpreting Internet-derived material, the human-Web interface, remote condition monitoring and many other areas.This volume presents the latest research on the interaction between intelligent systems (neural networks, adaptive and connectionist paradigms, fuzzy and rule-based systems, intelligent agents) and the Internet/WWW. It surveys both the employment of intelligent systems to facilitate and enhance the use of the Internet, and applications where the Internet is a channel through which intelligent techniques are applied.
(source: Nielsen Book Data)

• pt. I. Pattern Recognition
• Ch. 1. Multiple Classifier Systems
• Ch. 2. Building Decision Trees from the Fourier Spectrum of a Tree Ensemble
• Ch. 3. Clustering Large Data Sets
• Ch. 4. Multi-objective Variable String Genetic Classifier: Application to Remote Sensing Imagery
• pt. II. Image Processing and Vision
• Ch. 5. Dissimilarity Measures Between Fuzzy Sets or Fuzzy Structures
• Ch. 6. Early Vision: Concepts and Algorithms
• Ch. 7. Self-organizing Neural Network for Multi-level Image Segmentation
• Ch. 8. Geometric Transformation by Moment Method with Wavelet Matrix
• Ch. 9. New Computationally Efficient Algorithms for Video Coding.