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• 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)

• 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)

• 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.
(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)

• 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.

• 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)

• 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)

• 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)

• 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)

• 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)

• 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)

• 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.

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)

• 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)

• 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)

• 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)

• 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)

• 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.

• An introduction to artificial intelligence, N.G. Bourbakis
• fundamental methods for horn logic and AI applications, E. Kounalis and P. Marquis
• applications of genetic algorithms to permutation problems, F. Petry and B. Buckles
• extracting procedural knowledge from software systems using inductive learning in the PM system, R. Reynolds and E. Zannoni
• resource oriented parallel planning, S. Lee and K. Chung
• advanced parsing technology for knowledge based shells, J. Kipps
• analysis and synthesis of intelligent systems, W. Arden
• document analysis and recognition, S.N. Srihari et al
• signal understanding - an AI approach to modulation and classification, J.E. Whelchel et al
• and others.
• (source: Nielsen Book Data)

This volume is the first in a series which deals with the challenge of AI issues, gives updates of AI methods and applications, and promotes high quality new ideas, techniques and methodologies in AI. This volume contains articles by 38 specialists in various AI subfields covering theoretical and application issues.
(source: Nielsen Book Data)

• Chapter 1
• Welcome to TensorFlow.js Headings
• What is TensorFlow.js?
• TensorFlow.js API
• Tensors Operations Variables
• How to install it
• Use cases Chapter 2 Building your First Model Headings
• Building a logistic regression classification model
• Building a linear regression model
• Doing unsupervised learning with k-means
• Dimensionality reduction and visualization with t-SNE and d3.js
• Our first neural network Chapter 3 Create a drawing app to predict handwritten digits using Convolutional Neural Networks and MNIST Headings
• Convolutional Neural Networks
• The MNIST Dataset
• Design the model architecture
• Train the model
• Evaluate the model
• Build the drawing app
• Integrate the model within the app
• Chapter 4 "Move your body!" A game featuring PoseNet, a pose estimator model Headings
• What is PoseNet?
• Loading the model
• Interpreting the result
• Building a game around it Chapter 5 Detect yourself in real-time using an object detection model trained in Google Cloud's AutoML Headings
• TensorFlow Object Detection API
• Google Cloud's AutoML
• Training the model
• Exporting the model and importing it in TensorFlow.js
• Building the webcam app Chapter 6 Transfer Learning with Image Classifier and Voice Recognition Headings
• What's Transfer Learning?
• MobileNet and ImageNet (MobileNet is the base model and ImageNet is the training set)
• Transferring the knowledge
• Re-training the model
• Testing the model with a video Chapter 7 Censor food you do not like with pix2pix, Generative Adversarial Networks, and ml5.js Headings
• Introduction to Generative Adversarial Networks
• What is image translation?
• Training your custom image translator with pix2pix
• Deploying the model with ml5.js Chapter 8 Detect toxic words from a Chrome Extension using a Universal Sentence Encoder Headings
• Toxicity classifier
• Training the model
• Testing the model
• Integrating the model in a Chrome Extension Chapter 9 Time Series Analysis and Text Generation with Recurrent Neural Networks Headings
• Recurrent Neural Networks
• Example 1: Building an RNN for time series analysis
• Example 2: Building an RNN to generate text Chapter 10 Best practices, integrations with other platforms, remarks and final words Headings
• Best practices
• Integration with other platforms
• Materials for further practice
• Conclusion.
• (source: Nielsen Book Data)

Develop and deploy deep learning web apps using the TensorFlow.js library. TensorFlow. js is part of a bigger framework named TensorFlow, which has many tools that supplement it, such as TensorBoard , ml5js , tfjs-vis. This book will cover all these technologies and show they integrate with TensorFlow. js to create intelligent web apps. The most common and accessible platform users interact with everyday is their web browser, making it an ideal environment to deploy AI systems. TensorFlow.js is a well-known and battle-tested library for creating browser solutions. Working in JavaScript, the so-called language of the web, directly on a browser, you can develop and serve deep learning applications.You'll work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN). Through hands-on examples, apply these networks in use cases related to image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis. Also, these topics are very varied in terms of the kind of data they use, their output, and the training phase. Not everything in machine learning is deep networks, there is also what some call shallow or traditional machine learning. While TensorFlow.js is not the most common place to implement these, you'll be introduce them and review the basics of machine learning through TensorFlow.js. What You'll Learn Build deep learning products suitable for web browsers Work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN) Develop apps using image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis Who This Book Is For Programmers developing deep learning solutions for the web and those who want to learn TensorFlow.js with at least minimal programming and software development knowledge. No prior JavaScript knowledge is required, but familiarity with it is helpful.
(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)

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. The second edition of Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. In order to present a unified treatment of machine learning problems and solutions, it discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning. New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments; case studies available on the Web (with downloadable results for instructors); and many additional exercises. All chapters have been revised and updated. Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.
(source: Nielsen Book Data)

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. The second edition of Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. In order to present a unified treatment of machine learning problems and solutions, it discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning. New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments; case studies available on the Web (with downloadable results for instructors); and many additional exercises. All chapters have been revised and updated. Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.
(source: Nielsen Book Data)

• Preface.- Introduction.- Loss functions and their risks.- Surrogate loss functions.- Kernels and reproducing kernel Hilbert spaces.- Infinite samples versions of support vector machines.- Basic statistical analysis of SVMs.- Advanced statistical analysis of SVMs.- Support vector machines for classification.- Support vector machines for regression.- Robustness.- Computational aspects.- Data mining.- Appendix.- Notation and symbols.- Abbreviations.- Author index.- Subject index.- References.
• (source: Nielsen Book Data)

Every mathematical discipline goes through three periods of development: the naive, the formal, and the critical. David Hilbert The goal of this book is to explain the principles that made support vector machines (SVMs) a successful modeling and prediction tool for a variety of applications. We try to achieve this by presenting the basic ideas of SVMs together with the latest developments and current research questions in a uni?ed style. In a nutshell, we identify at least three reasons for the success of SVMs: their ability to learn well with only a very small number of free parameters, their robustness against several types of model violations and outliers, and last but not least their computational e?ciency compared with several other methods. Although there are several roots and precursors of SVMs, these methods gained particular momentum during the last 15 years since Vapnik (1995, 1998) published his well-known textbooks on statistical learning theory with aspecialemphasisonsupportvectormachines. Sincethen, the?eldofmachine learninghaswitnessedintenseactivityinthestudyofSVMs, whichhasspread moreandmoretootherdisciplinessuchasstatisticsandmathematics. Thusit seems fair to say that several communities are currently working on support vector machines and on related kernel-based methods. Although there are many interactions between these communities, we think that there is still roomforadditionalfruitfulinteractionandwouldbegladifthistextbook were found helpful in stimulating further research. Many of the results presented in this book have previously been scattered in the journal literature or are still under review. As a consequence, these results have been accessible only to a relativelysmallnumberofspecialists, sometimesprobablyonlytopeoplefrom one community but not the others.
(source: Nielsen Book Data)

• Methodologies Based on Particle Swarm Intelligence.- Swarm Intelligence: Foundations, Perspectives and Applications.- Waves of Swarm Particles (WoSP).- Grammatical Swarm: A Variable-Length Particle Swarm Algorithm.- SWARMs of Self-Organizing Polymorphic Agents.- Experiences Using Particle Swarm Intelligence.- Swarm Intelligence - Searchers, Cleaners and Hunters.- Ant Colony Optimisation for Fast Modular Exponentiation using the Sliding Window Method.- Particle Swarm for Fuzzy Models Identification.- A Matlab Implementation of Swarm Intelligence based Methodology for Identification of Optimized Fuzzy Models.
• (source: Nielsen Book Data)

Systems designers have learned that many agents co-operating within the system can solve very complex problems with a minimal design effort. In general, multi-agent systems that use swarm intelligence are said to be swarm intelligent systems. Today, these are mostly used as search engines and optimization tools. This volume reviews innovative methodologies of swarm intelligence, outlines the foundations of engineering swarm intelligent systems and applications, and relates experiences using the particle swarm optimisation.
(source: Nielsen Book Data)

• Part A: Particle Swarm Optimization. From Theory to Practice in Particle Swarm Optimization / Maurice Clerc
• What Makes Particle Swarm Optimization a Very Interesting and Powerful Algorithm? / J.L. Fernández-Martínez, E. García-Gonzalo
• Developing Niching Algorithms in Particle Swarm Optimization / Xiaodong Li
• Test Function Generators for Assessing the Performance of PSO Algorithms in Multimodal Optimization / Julio Barrera, Carlos A. Coello Coello
• Linkage Sensitive Particle Swarm Optimization / Deepak Devicharan, Chilukuri K. Mohan
• Parallel Particle Swarm Optimization Algorithm Based on Graphic Processing Units / Ying Tan, You Zhou
• Velocity Adaptation in Particle Swarm Optimization / Sabine Helwig, Frank Neumann, Rolf Wanka
• Integral-Controlled Particle Swarm Optimization / Zhihua Cui, Xingjuan Cai, Ying Tan, Jianchao Zeng
• Particle Swarm Optimization for Markerless Full Body Motion Capture / Zheng Zhang, Hock Soon Seah, Chee Kwang Quah
• An Adaptive Multi-Objective Particle Swarm Optimization Algorithm with Constraint Handling / Praveen Kumar Tripathi, Sanghamitra Bandyopadhyay, Sankar Kumar Pal
• Multiobjective Particle Swarm Optimization for Optimal Power Flow Problem / M.A. Abido
• A Multi-objective Resource Assignment Problem in Product Driven Supply Chain Using Quantum Inspired Particle Swarm Algorithm / Sri Krishna Kumar, S.G. Ponnambalam, M.K. Tiwari
• Part B: Bee Colony Optimization. Honeybee Optimisation -- An Overview and a New Bee Inspired Optimisation Scheme / Konrad Diwold, Madeleine Beekman, Martin Middendorf
• Parallel Approaches for the Artificial Bee Colony Algorithm / Rafael Stubs Parpinelli, César Manuel Vargas Benitez, Heitor Silvério Lopes
• Bumble Bees Mating Optimization Algorithm for the Vehicle Routing Problem / Yannis Marinakis, Magdalene Marinaki
• Part C: Ant Colony Optimization. Ant Colony Optimization: Principle, Convergence and Application / Haibin Duan
• Optimization of Fuzzy Logic Controllers for Robotic Autonomous Systems with PSO and ACO / Oscar Castillo, Patricia Melin, Fevrier Valdez, Ricardo Martínez-Marroquín
• Part D: Other Swarm Techniques. A New Framework for Optimization Based-On Hybrid Swarm Intelligence / Pei-Wei Tsai, Jeng-Shyang Pan, Peng Shi, Bin-Yih Liao
• Glowworm Swarm Optimization for Multimodal Search Spaces / K.N. Krishnanand, D. Ghose
• Direct and Inverse Modeling of Plants Using Cat Swarm Optimization / Ganapati Panda, Pyari Mohan Pradhan, Babita Majhi
• Reliability-Redundancy Optimization Using a Chaotic Differential Harmony Search Algorithm / Leandro dos Santos Coelho, Diego L. de A. Bernert, Viviana Cocco Mariani
• Gene Regulatory Network Identification from Gene Expression Time Series Data Using Swarm Intelligence / Debasish Datta, Amit Konar, Swagatam Das, B.K. Panigrahi.

Recent work on the behavior of swarming creatures such as bees posits an innate collective intelligence that gives rise to myriad computational problem-solving techniques. This volume is both an introduction to the topic and a survey of leading-edge research.

• Preface Acknowledgment PART I. Theoretical Foundation
• Chapter 1. The Goal of Artificial Intelligence 1.1 To define intelligence 1.2 Various schools in AI research 1.3 AI as a whole
• Chapter 2. A New Approach Toward AI 2.1 To define AI 2.2 Intelligent reasoning systems 2.3 Major design issues of NARS PART II. Non-Axiomatic Reasoning System
• Chapter 3. The Core Logic 3.1 NAL-0: binary inheritance 3.2 The language of NAL-1 3.3 The inference rules of NAL-1
• Chapter 4. First-Order Inference 4.1 Compound terms 4.2 NAL-2: sets and variants of inheritance 4.3 NAL-3: intersections and differences 4.4 NAL-4: products, images, and ordinary relations
• Chapter 5. Higher-Order Inference 5.1 NAL-5: statements as terms 5.2 NAL-6: statements with variables 5.3 NAL-7: temporal statements 5.4 NAL-8: procedural statements
• Chapter 6. Inference Control 6.1 Task management 6.2 Memory structure 6.3 Inference processes 6.4 Budget assessment . PART III. Comparison and Discussion
• Chapter 7. Semantics 7.1 Experience vs. model 7.2 Extension and intension 7.3 Meaning of term 7.4 Truth of statement
• Chapter 8. Uncertainty 8.1 The non-numerical approaches 8.2 The fuzzy approach 8.3 The Bayesian approach 8.4 Other probabilistic approaches 8.5 Unified representation of uncertainty
• Chapter 9. Inference Rules 9.1 Deduction 9.2 Induction 9.3 Abduction 9.4 Implication
• Chapter 10. NAL as a Logic 10.1 NAL as a term logic 10.2 NAL vs. predicate logic 10.3 Logic and AI
• Chapter 11. Categorization and Learning 11.1 Concept and categorization 11.2 Learning in NARS
• Chapter 12. Control and Computation 12.1 NARS and theoretical computer science 12.2 Various assumptions about resources 12.3 Dynamic natures of NARS PART IV. Conclusions
• Chapter 13. Current Results 13.1 Theoretical foundation 13.2 Formal model 13.3 Computer implementation
• Chapter 14. NARS in the Future 14.1 Next steps of the project 14.2 What NARS is not 14.3 General implications Bibliography Index.
• (source: Nielsen Book Data)

This book is the most comprehensive description of the decades-long Non-Axiomatic Reasoning System (NARS) project, including its philosophical foundation, methodological consideration, conceptual design details, implications in the related fields, and its similarities and differences to many related works in cognitive science. While most current works in Artificial Intelligence (AI) focus on individual aspects of intelligence and cognition, NARS is designed and developed to attack the AI problem as a whole.
(source: Nielsen Book Data)

• Intelligent Agents: Integrating Multiple Components Through a Symbolic Structure / Razvan Dinu, Tiberiu Stratulat
• An Architecture for Multi-Dimensional Temporal Abstraction Supporting Decision Making in Oil-Well Drilling / Odd Erik Gundersen, Frode Sørmo
• A New Impulse Noise Filtering Algorithm Based on a Neuro-Fuzzy Network / Yueyang Li, Haichi Luo, Jun Sun
• A Fuzzy System for Educational Tasks for Children with Reading and Writing Disabilities / Adalberto Bosco C. Pereira
• Optimizing the Performance of a Refrigeration System Using an Invasive Weed Optimization Algorithm / Roozbeh Razavi-Far, Vasile Palade, Jun Sun
• A New Cooperative Evolutionary Multi-Swarm Optimizer Algorithm Based on CUDA Architecture Applied to Engineering Optimization / Daniel Leal Souza, Otávio Noura Teixeira
• Hybrid Approach of Genetic Programming and Quantum-Behaved Particle Swarm Optimization for Modeling and Optimization of Fermentation Processes / Jun Sun, Vasile Palade, Zhenyu Wang
• Hybrid Client Specific Discriminant Analysis and its Application to Face Verification / Xiao-Qi Sun, Xiao-Jun Wu, Jun Sun.

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. The 3rd Workshop on ""Combinations of Intelligent Methods and Applications"" (CIMA 2012) 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 2012 was held in conjunction with the 22nd European Conference on A.

• Title Page; Preface; Conference Organization; Contents; Invited Talks; Some Real-World Applications of Soft Artificial Intelligence: Scientogram Mining, Assembly Line Balancing, and Forensic Identification; Challenges of Automation and Safety in Field Robotics; KDD, DM and Machine Learning; Data Mining and Query Answer Techniques Applied to a Bio-Nutritional Trials Focused Expert System; The Use of the Traffic Lights Panel as a Goodness-of-Clustering Indicator: An Application to Financial Assets

• Using Gabriel Graphs in Borderline-SMOTE to Deal with Severe Two-Class Imbalance Problems on Neural NetworksActive Learning of Actions Based on Support Vector Machines; Towards the Formalization of Re-Identification for Some Data Masking Methods; Natural Language Processing and Recommenders; Towards Object Descriptions in Natural Language from Qualitative Models; Semantically-Enhanced Recommenders; Computer Vision; Supervised Texture Classification Using Optimization Techniques; Modelling Facial Expressions Dynamics with Gaussian Process Regression; Survey on 2D and 3D Human Pose Recovery

• A Study of Registration Techniques for 6DoF SLAMRobotics; Object Detection Methods for Robot Grasping: Experimental Assessment and Tuning; The Role of i-Walker in Post-Stroke Training; Using a RGB-D Camera for 6DoF SLAM; Learning Topological SLAM Using Visual Information; AI for Optimization Problems; Exploring Genetic Algorithms and Simulated Annealing for Immobile Location-Allocation Problem; Analysis and Generation of Pseudo-Industrial MaxSAT Instances; A SAT-Based Approach to MinSAT; Multicast Session Protection Planner
• Tool to Plan and Deploy Protection Infrastructure: A SPEA Approach

• AI Applications to Real WorldA Case-Based Hybrid System for Injection Molding Sensorization; First Studies on Self-Preserving Digital Objects; Intelligent Building Energy Management Through Holistic Knowledge Based Approach; Quantitative and Qualitative Approaches for Stock Movement Prediction; Subject Index; Author Index

• On the History of the Growth Optimal Portfolio (M M Christensen)
• Empirical Log-Optimal Portfolio Selections: A Survey (L Gyorfi et al.)
• Log-Optimal Portfolio Selection with Proportional Transaction Costs (L Gyorfi & H Walk)
• Log-Optimal Portfolio with Short Selling and Leverage (M Horvath & A Urban)
• Nonparametric Sequential Prediction of Stationary Time Series (L Gyorfi & G Ottuscak)
• Empirical Pricing American Put Options (L Gyorfi & A Telcs).
• (source: Nielsen Book Data)

This volume investigates algorithmic methods based on machine learning in order to design sequential investment strategies for financial markets. Such sequential investment strategies use information collected from the market's past and determine, at the beginning of a trading period, a portfolio; that is, a way to invest the currently available capital among the assets that are available for purchase or investment.The aim is to produce a self-contained text intended for a wide audience, including researchers and graduate students in computer science, finance, statistics, mathematics, and engineering.
(source: Nielsen Book Data)

• On the History of the Growth Optimal Portfolio (M M Christensen)
• Empirical Log-Optimal Portfolio Selections: A Survey (L Gyorfi et al.)
• Log-Optimal Portfolio Selection with Proportional Transaction Costs (L Gyorfi & H Walk)
• Log-Optimal Portfolio with Short Selling and Leverage (M Horvath & A Urban)
• Nonparametric Sequential Prediction of Stationary Time Series (L Gyorfi & G Ottuscak)
• Empirical Pricing American Put Options (L Gyorfi & A Telcs).
• (source: Nielsen Book Data)

This volume investigates algorithmic methods based on machine learning in order to design sequential investment strategies for financial markets. Such sequential investment strategies use information collected from the market's past and determine, at the beginning of a trading period, a portfolio; that is, a way to invest the currently available capital among the assets that are available for purchase or investment.The aim is to produce a self-contained text intended for a wide audience, including researchers and graduate students in computer science, finance, statistics, mathematics, and engineering.
(source: Nielsen Book Data)

• Tile Page; Preface; Organization; Contents; An Assistance Infrastructure for Open MAS; On the Modularity of Industrial SAT Instances; On 2SAT-MaxOnes with Unbalanced Polarity: From Easy Problems to Hard MaxClique Problems; Experimenting with the Instances of the MaxSAT Evaluation; Lazy Learning Methods for Quality of Life Assessment in People with Intellectual Disabilities; Probabilistic Appearance-Based Mapping and Localization Using the Feature Stability Histogram; Towards an Efficient Use of Resources in All-Optical Networks; Depth of Valleys Accumulation Algorithm for Object Detection.

This book is a collection of the papers accepted for presentation at the 14th International Conference of the Catalan Association for Artificial Intelligence (CCIA 2011), held at the University of Lleida in October 2011. From the 28 papers submitted, 24 were accepted for inclusion in this book and for oral presentation at the conference after a careful reviewing process. The conference also featured invited talks by two outstanding international researchers: Christian Bessiere of CNRS, University of Montpellier, France, and Bart Selman of Cornell University, USA. The CCIA conference brings tog.

• Machine generated contents note: Invited Talks
• Playing Games with Games / Michael Wooldridge
• User-Generated AI for Interactive Digital Entertainment / Ashwin Ram
• Perspectives on Artificial Intelligence in a Business Environment / Peter Nordin
• Tutorials
• Probabilistic Decision Graphs for Optimization Under Uncertainty / Finn Verner Jensen
• Coordinating Multi-Agent Systems Using Social Laws / Thomas Agotnes
• Full Papers
• Machine Learning
• User-Oriented Assessment of Classification Model Understandability / Niklas Lavesson
• Concurrent Learning of Large-Scale Random Forests / Henrik Bostrom
• Learning Multi-Label Predictors Under Sparsity Budget / Tapio Salakoski
• Machine Learning Methods for Spatial Clustering on Precision Agriculture Data / Rudolf Kruse
• Planning
• On Constraint Models for Parallel Planning: The Novel Transition Scheme / Roman Bartak
• Trajectory Planning on Grids: Considering Speed Limit Constraints / Lukas Chrpa
• Safe Reinforcement Learning for Continuous Spaces Through Lyapunov-Constrained Behavior / Erik Kyrkjebø
• Parallel Monte Carlo Tree Search on GPU / Reiji Suda
• Exploiting Global Properties in Path-Consistency Applied on SAT / Pavel Surynek
• Applications
• Incremental Stream Clustering for Anomaly Detection and Classification / Jan Ekman
• Respiratory Motion Prediction: A Fuzzy Logic Approach / Manish Kakar
• Case-Based Reasoning in a System Architecture for Intelligent Fish Farming / Agnar Aamodt
• Overview of Fault Detection Techniques in Automated Monitoring Systems / Shengtong Zhong
• Robotics and Cognition
• View-Independent Human Gait Recognition Using CBR and HMM / Odd Erik Gundersen
• Self-Exploration of Autonomous Robots Using Attractor-Based Behavior Control and ABC-Learning / Matthias Kubisch.

• pt. 1. Basic principles
• pt. 2. Continuous time swarms
• pt. 3. Discrete time swarms
• pt. 4. Swarm based optimization methods.

Swarming species such as flocks of birds or schools of fish exhibitfascinating collective behaviors during migration and predatoravoidance. Similarly, engineered multi-agent dynamic systems such as groups of autonomous ground, underwater, or air vehicles ("vehicle swarms") exhibit sophisticated collective behaviors while maneuvering. In this book we show how to model and control a wide range of suchmulti-agent dynamic systems and analyze their collective behaviorusing both stability theoretic and simulation-based approaches. Inparticular, we investigate problems such as group aggregat

• Title page; Preface; Conference Organization; Contents; Invited Talks; Agents and Multi-Agents Systems; AI Real-World Applications; Data Mining, Machine Learning and Soft Computing; Logics, Constraint Satisfaction and Reasoning; Robotics, Vision and Perception; Subject Index; Author Index.

Artificial Intelligence (AI) is one of the hottest areas of research with hundreds of potentially real life applications and one of the disciplines that is most intertwined with all sorts of fields related to human experience. A wide range of disciplines, sectors and technologies could benefit from the results of AI breakthroughs; from robotics to search engines; healthcare or environment; screening, monitoring or supervision techniques. This book compiles the 33 accepted papers presented at the 13th International Conference of the Catalan Association for Artificial Intelligence. Although most o.

• New Hybrid Intelligent Systems to Solve Linear and Quadratic Optimization Problems and Increase Guaranteed Optimal Convergence Speed of Recurrent ANN
• A Novel Optimization Algorithm Based on Reinforcement Learning
• The Use of Opposition for Decreasing Function Evaluations in Population-Based Search
• Search Procedure Exploiting Locally Regularized Objective Approximation. A Convergence Theorem for Direct Search Algorithms
• Optimization Problems with Cardinality Constraints
• Learning Global Optimization Through a Support Vector Machine Based Adaptive Multistart Strategy
• Multi-Objective Optimization Using Surrogates
• A Review of Agent-Based Co-Evolutionary Algorithms for Multi-Objective Optimization
• A Game Theory-Based Multi-Agent System for Expensive Optimisation Problems
• Optimization with Clifford Support Vector Machines and applications
• A Classification method based on principal component analysis and differential evolution algorithm applied for prediction diagnosis from clinical EMR heart data sets
• An Integrated Approach to Speed Up GA-SVM Feature Selection Model
• Computation in Complex Environments;
• Project Scheduling: Time-Cost Tradeoff Problems
• Systolic VLSI and FPGA Realization of Artificial Neural Networks
• Application of Coarse-Coding Techniques for Evolvable Multirobot Controllers.

This collection of recent studies spans a range of computational intelligence applications, emphasizing their application to challenging real-world problems. Covers Intelligent agent-based algorithms, Hybrid intelligent systems, Machine learning and more.

• Invited Presentations.- Property Testing: A Learning Theory Perspective.- Spectral Algorithms for Learning and Clustering.- Unsupervised, Semisupervised and Active Learning I.- Minimax Bounds for Active Learning.- Stability of k-Means Clustering.- Margin Based Active Learning.- Unsupervised, Semisupervised and Active Learning II.- Learning Large-Alphabet and Analog Circuits with Value Injection Queries.- Teaching Dimension and the Complexity of Active Learning.- Multi-view Regression Via Canonical Correlation Analysis.- Statistical Learning Theory.- Aggregation by Exponential Weighting and Sharp Oracle Inequalities.- Occam's Hammer.- Resampling-Based Confidence Regions and Multiple Tests for a Correlated Random Vector.- Suboptimality of Penalized Empirical Risk Minimization in Classification.- Transductive Rademacher Complexity and Its Applications.- Inductive Inference.- U-Shaped, Iterative, and Iterative-with-Counter Learning.- Mind Change Optimal Learning of Bayes Net Structure.- Learning Correction Grammars.- Mitotic Classes.- Online and Reinforcement Learning I.- Regret to the Best vs. Regret to the Average.- Strategies for Prediction Under Imperfect Monitoring.- Bounded Parameter Markov Decision Processes with Average Reward Criterion.- Online and Reinforcement Learning II.- On-Line Estimation with the Multivariate Gaussian Distribution.- Generalised Entropy and Asymptotic Complexities of Languages.- Q-Learning with Linear Function Approximation.- Regularized Learning, Kernel Methods, SVM.- How Good Is a Kernel When Used as a Similarity Measure?.- Gaps in Support Vector Optimization.- Learning Languages with Rational Kernels.- Generalized SMO-Style Decomposition Algorithms.- Learning Algorithms and Limitations on Learning.- Learning Nested Halfspaces and Uphill Decision Trees.- An Efficient Re-scaled Perceptron Algorithm for Conic Systems.- A Lower Bound for Agnostically Learning Disjunctions.- Sketching Information Divergences.- Competing with Stationary Prediction Strategies.- Online and Reinforcement Learning III.- Improved Rates for the Stochastic Continuum-Armed Bandit Problem.- Learning Permutations with Exponential Weights.- Online and Reinforcement Learning IV.- Multitask Learning with Expert Advice.- Online Learning with Prior Knowledge.- Dimensionality Reduction.- Nonlinear Estimators and Tail Bounds for Dimension Reduction in l 1 Using Cauchy Random Projections.- Sparse Density Estimation with ?1 Penalties.- ?1 Regularization in Infinite Dimensional Feature Spaces.- Prediction by Categorical Features: Generalization Properties and Application to Feature Ranking.- Other Approaches.- Observational Learning in Random Networks.- The Loss Rank Principle for Model Selection.- Robust Reductions from Ranking to Classification.- Open Problems.- Rademacher Margin Complexity.- Open Problems in Efficient Semi-supervised PAC Learning.- Resource-Bounded Information Gathering for Correlation Clustering.- Are There Local Maxima in the Infinite-Sample Likelihood of Gaussian Mixture Estimation?.- When Is There a Free Matrix Lunch?.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 20th Annual Conference on Learning Theory, COLT 2007, held in San Diego, CA, USA in June 2007. It covers unsupervised, semisupervised and active learning, statistical learning theory, inductive inference, regularized learning, kernel methods, SVM, online and reinforcement learning, learning algorithms and limitations on learning, dimensionality reduction, as well as open problems.
(source: Nielsen Book Data)

• Title page; ECCAI Member Societies; Conference Organization; ECAI Programme Committee; Additional Reviewers; Preface; Contents; Invited Talks; Socially Intelligent Robots; Managing Diversity in Knowledge; The Truth About Defaults; SmartWeb: Getting Answers on the Go; Papers; Cognitive Modelling; Constraints and Search; Distributed AI/Agents; Knowledge Representation and Reasoning; Machine Learning; Natural Language Processing; Planning and Scheduling; PAIS; Perception; Robotics; Posters; Cognitive Modeling; Constraints and Search; Distributed AI/Agents; Knowledge Representation and Reasoning.

In the summer of 1956, John McCarthy organized the famous Dartmouth Conference which is commonly viewed as the founding event for the field of Artificial Intelligence. Knowledge Representation & Reasoning is traditionally strong in Europe and remains the biggest area of ECAI 2006.

• A new way to acquire knowledge, H.-Y. Wang
• an SPN knowledge representation scheme, J. Gattiker and N. Bourbakis
• on the deep structures of word problems and their construction, F. Gomez
• resolving conflicts in inheritance reasoning with statistical approach, C. Lee
• integrating high and low level computer vision for scene understanding, R. Malik and S. So
• the evolution of commercial AI tools - the first decade, F. Hayes-Roth
• reengineering - the AT generation - billions on the table, J.S. Minor, Jr.
• an intelligent tool for discovering data dependencies in relational DBS, P. Gavaskar and F. Golshani
• a case-based reasoning (CBR) tool to assist traffic flow, B. Das and S. Bayles
• a study of financial expert system based on flops, T. Kaneko and K. Takenaka
• an associative data parallel compilation model for tight integration of high performance knowledge retrieval and computation, A. Bansal
• software automation - from silly to intelligent, X. Jiafu et al
• software engineering using artificial intelligence - the knowledge based software assistant, D. White
• knowledge based derivation of programmes from specs, T. Weight et al
• automatic functional model generation for parallel fault design error simulations, S.E. Chang and S. Szygenda
• visual reverse engineering using SPN for automated diagnosis and functional simulation of digital circuits, J. Gattiker and S. Mertoguno
• the impact of AI in VLSI design automation, M. Mortazavi and N. Bourbakis
• the automated acquisition of subcategorization of verbs, nouns and adjectives from sample sentences, F. Gomez
• general method for planning and rendezvous problems, K. Trovato
• learning to improve path planning performance, P.C. Chen
• incremental adaptation as a method to improve reactive behaviour, A.J. Hendriks and D.M. Lyons
• an SPN-neural planning methodology for coordination of multiple robotic arms with constrained placement, N. Bourbakis and A. Tascillo.
• (source: Nielsen Book Data)

Covering artificial intelligence and automation, these contributions discuss: the evolution of AI tools; an SPN knowledge representation scheme; software engineering using AI; the impact of AI in VLSI design automation; incremental adaptation as a method to improve reactive behaviour; and more.
(source: Nielsen Book Data)

• An intelligent image-based computer-aided education system: the prototype BIRDS / A.A. David, O. Thiery & M. Crehange
• PLAYMAKER: a knowledge-based approach to characterizing hydrocarbon plays / G. Biswas [and others]
• An expert system for interpreting mesoscale features in oceanographic satellite images / N. Krishnakumar [and others]
• An expert system for tuning particle beam accelerators / D.L. Lager, H.R. Brand & W.J. Maurer
• Expert system approach to assessments of bleeding predispositions in tonsillectomy/adenoidectomy patients / N.J. Pizzi & J.M. Gerrard
• Expert system approach using graph representation and analysis for variable-stroke internal-combustion engine design / S.N.T. Shen, M.S. Chew & G.F. Issa
• A comparison of two new techniques for conceptual clustering / S.L. Crawford & S.K. Souders
• Querying an object-oriented database using free language / P. Trigano [and others]
• Adaptive planning for air combat maneuvering / I.C. Hayslip, J.P. Rosenking & J. Filbert
• AM/AG model: a hierarchical social system metaphor for distributed problem solving / D.G. Shin & J. Leone
• CAUSA
• A tool for model-based knowledge acquisition / W. Dilger & J. Moller
• PRIOPS: a real-time production system architecture for programming and learning in embedded systems / D.E. Parson & G.D. Blank.

This volume contains a well-balanced set of applications and theory papers in artificial intelligence advances. The applications papers each discuss a system that is (or is close to being) a fielded system that solves real problems using one or more AI techniques. They cover areas such as education, physics, energy, control, medicine and mechanical engineering.The theory papers, representing recent advances in various theoretical aspects of AI technology, concern themselves with "building block" issues, i.e. theories, algorithms, architectures, and software tools that can or will be used for modules within future systems. The topics covered are: clustering, natural language, adaptive algorithms, distributed processing, knowledge acquisition, and systems programming.
(source: Nielsen Book Data)

• Identification of the Conjugate Pair to Estimating Object Distance: An Application of the Ant Colony Algorithm.-Design of Palm Acupuncture Points Indicator.- Low-rank Representation and Locality-constrained Regression for Robust Low-Resolution Face Recognition.- Face Recognition Benchmark with ID Photos.- Scene Relighting using a Single Reference Image through Material Constrained Layer Decomposition.- Applying Adaptive Actor-critic Learning to Human Upper Lime Lifting Motion.- A Demand-based Allocation Mechanism for Virtual Machine.- A Joint Hierarchy Model for Action Recognition Using Kinect.- QoS-Based Medical Program Evolution.- An Improved 3D Surface Reconstruction Method based on Three Wavelength Phase Shift Profilometry.- The Research on the Lung Tumor Imaging based on the Electrical Impedance Tomography.- Combining CNN and MRF for Road Detection.- The Increasing of Discrimination Accuracy of Waxed Apples based on Hyperspectral Imaging Optimized by Spectral Correlation Analysis.- A Diffeomorphic Demons Approach to Statistical Shape Modeling.
• (source: Nielsen Book Data)

This book highlights selected papers presented at the 2nd International Symposium on Artificial Intelligence and Robotics 2017 (ISAIR2017), held in Nakamura Centenary Memorial Hall, Kitakyushu, Japan on November 25-26, 2017. Today, the integration of artificial intelligence and robotic technologies has become a topic of growing interest for both researchers and developers from academic fields and industries worldwide, and artificial intelligence is poised to become the main approach pursued in next-generation robotics research. The rapidly growing number of artificial intelligence algorithms and big data solutions has significantly extended the number of potential applications for robotic technologies. However, it also poses new challenges for the artificial intelligence community. The aim of this symposium is to provide a platform for young researchers to share the latest scientific achievements in this field, which are discussed in these proceedings.
(source: Nielsen Book Data)

• 1. Swarm Intelligence and Bio-Inspired Computation: An Overview
• 2. Review and Analysis of Swarm-intelligence Based Algorithms 3. Levy Flights and Global Optimization 4. Self-Adaptive Memetic Firefly Algorithm 5. Modelling and Simulation of Labor Division in An Ant Colony: A Problem-Oriented Approach 6. Particle Swarm Optimization and Their Variants: Convergence and Applications 7. A Survey of Swarm Algorithms Applied to Discrete Optimization Problems 8. A Comprehensive Survey of Test Functions for Global Optimization 9. Binary Bat Algorithm for Feature Selection 10. Intelligent Music Composition 11. The Development and Applications of the Cuckoo Search Algorithm 12. Bio-Inspired Models and the Semantic Web 13. Discrete Firefly Algorithm for Travelling Salesman Problem: A New Movement Scheme 14. Modelling to Generate Alternatives Using Biologically-Inspired Algorithms 15. Structural Optimization Using Krill Herd Algorithm 16. Artificial Plant Optimization Algorithm 17. Genetic Algorithms for the Berth Allocation Problem in Real Time 18. Opportunities and Challenges of Integrating Bio-Inspired Optimization and Data Mining Algorithms 19. Improvement of PSO Algorithm by Memory Based Gradient Search: Application in Inventory Management.
• (source: Nielsen Book Data)

• 5.2.2.3 Time-Dependent Response Threshold Model
• 5.2.3 Some Analysis
• 5.3 Modeling and Simulation of Ant Colony's Labor Division with Multitask
• 5.3.1 Background Analysis
• 5.3.2 Design and Implementation of Ant Colony's Labor Division Model with Multitask
• 5.3.2.1 Design of Ant Colony's Labor Division Model with Multitask
• Environmental Stimuli
• Agent Attributes
• Probability of Participation and Exit
• Simulation Principle
• 5.3.2.2 Implementation of Ant Colony's Labor Division Model with Multitask
• 5.3.3 Supply Chain Virtual Enterprise Simulation
• 5.3.3.1 Simulation Example and Parameter Settings
• 5.3.3.2 Simulation Results and Analysis
• 5.3.4 Virtual Organization Enterprise Simulation
• 5.3.4.1 Simulation Example and Parameter Settings
• 5.3.4.2 Simulation Results and Analysis
• 5.3.5 Discussion
• 5.4 Modeling and Simulation of Ant Colony's Labor Division with Multistate
• 5.4.1 Background Analysis
• 5.4.2 Design and Implementation of Ant Colony's Labor Division Model with Multistate
• 5.4.2.1 Design of Ant Colony's Labor Division Model with Multistate
• Stimulus Values in Multitask Environment
• Relative Environment Stimulus Value sαβ and Relative Threshold θαβ
• Agent State Transformation
• 5.4.2.2 Implementation of Ant Colony's Labor Division Model with Multistate
• 5.4.3 Simulation Example of Ant Colony's Labor Division Model with Multistate
• 5.4.3.1 Simulation and Experiment Environment
• 5.4.3.2 Parameters of the Simulation Model
• 5.4.3.3 Simulation Results
• 5.4.3.4 Analysis of Results
• 5.5 Modeling and Simulation of Ant Colony's Labor Division with Multiconstraint
• 5.5.1 Background Analysis
• 5.5.2 Design and Implementation of Ant Colony's Labor Division Model with Multiconstraint
• 5.5.2.1 Design of Ant Colony's Labor Division Model with Multiconstraint.

Swarm Intelligence and bio-inspired computation have become increasing popular in the last two decades. Bio-inspired algorithms such as ant colony algorithms, bat algorithms, bee algorithms, firefly algorithms, cuckoo search and particle swarm optimization have been applied in almost every area of science and engineering with a dramatic increase of number of relevant publications. This book reviews the latest developments in swarm intelligence and bio-inspired computation from both the theory and application side, providing a complete resource that analyzes and discusses the latest and future trends in research directions. It can help new researchers to carry out timely research and inspire readers to develop new algorithms. With its impressive breadth and depth, this book will be useful for advanced undergraduate students, PhD students and lecturers in computer science, engineering and science as well as researchers and engineers.
(source: Nielsen Book Data)

• Preface; Acknowledgments;
• Chapter 1. Introduction; 1.1 Background; 1.2 Cognitive Architectures; 1.3 Soar; 1.4 Research Strategy; 1.5 Preview of Chapters 2-14;
• Chapter 2. Requirements for Cognitive Architectures; 2.1 Characteristics of Environments, Tasks, and Agents; 2.2 Architectural Requirements;
• Chapter 3. The Problem-Space Computational Model; 3.1 Task Environments; 3.2 The Problem-Space Framework; 3.3 Knowledge Search; 3.4 Problem-Space Computational Models; 3.5 Impasses and Substates; 3.6 Using Multiple Sources of Knowledge; 3.7 Acquiring Knowledge; 3.8 Alternative PSCMs

• Chapter 4. Soar as an Implementation of the PSCM4.1 Production Systems; 4.2 Mapping Production Systems onto the PSCM; 4.3 The Soar Processing Cycle; 4.4 Demonstrations of Basic PSCM; 4.5 Discussion; 4.6 Analysis of Requirements;
• Chapter 5. Impasses and Substates: The Basis for Complex Reasoning; 5.1 Impasses; 5.2 Substates; 5.3 Problem Solving in Substates; 5.4 Substate Results; 5.5 Maintaining Consistency; 5.6 Demonstrations of Impasses and Substates; 5.7 Discussion; 5.8 Analysis of Requirements;
• Chapter 6. Chunking; 6.1 Chunking in Soar; 6.2 Implications of Chunking in Soar

• 6.3 Demonstrations of Chunking6.4 Assumptions Inherent to Chunking;
• Chapter 7. Tuning Procedural Knowledge: Reinforcement Learning; 7.1 Reinforcement Learning in Soar; 7.2 Learning over Large State Spaces; 7.3 Demonstrations of Reinforcement Learning; 7.4 Analysis of Requirements;
• Chapter 8. Semantic Memory; 8.1 Semantic Memory in Soar; 8.2 Encoding and Storage; 8.3 Retrieval; 8.4 Demonstrations of Semantic Memory; 8.5 Analysis of Requirements;
• Chapter 9. Episodic Memory; 9.1 Episodic Memory in Soar; 9.2 Encoding and Storage; 9.3 Retrieval; 9.4 Use of Episodic Memory

• 9.5 Demonstrations of Episodic Memory9.6 Comparison of Episodic Memory and Semantic Memory; 9.7 Analysis of Requirements;
• Chapter 10. Visuospatial Processing with Mental Imagery; 10.1 Visual and Spatial Representations; 10.2 Visuospatial Domains; 10.3 SVS; 10.4 Demonstrations of Spatial and Visual Imagery; 10.5 Analysis of Requirements;
• Chapter 11. Emotion; 11.1 Appraisal Theories of Emotion; 11.2 Abstract Functional Cognitive Operations; 11.3 Unifying Cognitive Control and Appraisal; 11.4 Emotion, Mood, and Feeling; 11.5 Emotion and Reinforcement Learning

• 11.6 Demonstrations of Emotion Processing11.7 Analysis of Requirements;
• Chapter 12. Demonstrations of Multiple Architectural Capabilities; 12.1 Learning to Use Episodic Memory with Reinforcement Learning; 12.2 Using Mental Imagery with Reinforcement Learning; 12.3 Diverse Forms of Action Modeling; 12.4 Analysis of Requirements;
• Chapter 13. Soar Applications; 13.1 Applications; 13.2 TacAir-Soar; 13.3 Imagining TacAir-Soar 2.0;
• Chapter 14. Conclusion; 14.1 Soar from a Structural Perspective; 14.2 Soar from a Functional Perspective; 14.3 Evaluating Soar on Architectural Requirements; References

The definitive presentation of Soar, one AI's most enduring architectures, offering comprehensive descriptions of fundamental aspects and new components. In development for thirty years, Soar is a general cognitive architecture that integrates knowledge-intensive reasoning, reactive execution, hierarchical reasoning, planning, and learning from experience, with the goal of creating a general computational system that has the same cognitive abilities as humans. In contrast, most AI systems are designed to solve only one type of problem, such as playing chess, searching the Internet, or scheduling aircraft departures. Soar is both a software system for agent development and a theory of what computational structures are necessary to support human-level agents. Over the years, both software system and theory have evolved. This book offers the definitive presentation of Soar from theoretical and practical perspectives, providing comprehensive descriptions of fundamental aspects and new components. The current version of Soar features major extensions, adding reinforcement learning, semantic memory, episodic memory, mental imagery, and an appraisal-based model of emotion. This book describes details of Soar's component memories and processes and offers demonstrations of individual components, components working in combination, and real-world applications. Beyond these functional considerations, the book also proposes requirements for general cognitive architectures and explicitly evaluates how well Soar meets those requirements.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Machine learning and statistics overview
• 3. Performance measures I
• 4. Performance measures II
• 5. Error estimation
• 6. Statistical significance testing
• 7. Data sets and experimental framework
• 8. Recent developments
• 9. Conclusion
• Appendix A: statistical tables
• Appendix B: additional information on the data
• Appendix C: two case studies.
• (source: Nielsen Book Data)

The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA, facilitating better practical insight as well as implementation. Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for conducting performance evaluations of algorithms in practical settings.
(source: Nielsen Book Data)

• Acknowledgments Douglas Hofstadter: Foreword Stefano Franchi and Francesco Bianchini: Introduction: On the Historical Dynamics of Cognitive Science: a View from the Periphery The cybernetic suburb Stefan Franchi: Life, Death, and Resurrection of the Homeostat Peter Galison: The Ontology of the Enemy: Norbert Wiener and the Cybernetic Vision Peter Asaro: Computers as Models of the Mind: On Simulations, Brains, and the Design of Computers AI's peripheries Claudio Pogliano: At the Periphery of the Rising Empire: the Case of Italy (1945-1968) Patrice Maniglier: Processing Cultures: "Structuralism" in the History of Artificial Intelligence Slava Gerovitch: Artificial Intelligence With a National Face: American and Soviet Cultural Metaphors for Thought Margins of computations Francesco Bianchini: The Cartesian-Leibnizian Turing Test Maurizio Matteuzzi: Turing Computability and Leibniz Computability Christopher M. Kelty: Logical Instruments: Regular Expressions, AI, and Thinking about Thinking At the thresholds of computability Solomon Feferman: Goedel, Nagel, Minds, and Machines Rossella Lupacchini: Entangling Effective Procedures: From Logic Machines to Quantum Automata Giorgio Sandri: Turing 1948 vs. Goedel 1972 Works Cited Index About the Contributors.
• (source: Nielsen Book Data)

The book brings into relief the variety of approaches and disciplines that have informed the quest for a theory of cognition. The center of interest are the historical, geographical, and theoretical peripheries of classic AI's mainstream research program. The twelve chapters bring back into focus the variety of strategies and theoretical questions that researchers explored while working toward a scientific theory of cognition and pre-cognition. The volume is organized in four parts, each one including three essays. The first one deals with cybernetics, the approach that may be considered as the most important periphery of classic AI research. The second part focuses on the geographical periphery of AI research. It examines how the theories and techniques developed on AI's home ground were translated into countries with different cultures and traditions: Italy, France, and the Soviet Union. The third part focuses on AI's periphery understood in the cultural and historical meaning of the term. It contains essays that locate some of the central concepts of AI, like representation and computability, within a broader philosophical (Descartes, Aristotle, Leibniz) and technical background (programming theory and practice). The fourth and final part of the volume is focused directly on the limitation of Turing's classic computability theory and its possible alternatives, some of which were studied in the early years of AI's research (e.g. Ashby's re-entrant information model), while others have been intensely studied in recent times (quantum automata).
(source: Nielsen Book Data)

• Foreword; Preface; Contents; Argumentation Theory: A Very Short Introduction; Douglas Walton; Description Logic; Part I Abstract Argument Systems; Pietro Baroni and Massimiliano Giacomin; Semantics of Abstract Argument Systems; Bayesian Networks; Abstract Argumentation and Values; Trevor Bench-Capon and Katie Atkinson; Bipolar abstract argumentation systems; Claudette Cayrol and Marie-Christine Lagasquie-Schiex; Complexity of Abstract Argumentation; Paul E. Dunne and Michael Wooldridge; Proof Theories and Algorithms for Abstract Argumentation Frameworks; Sanjay Modgil and Martin Caminada.

Examines the intersection between two fields of inquiry, including Argumentation Theory and Artificial Intelligence. This book presents an overview of key concepts in argumentation theory and of formal models of argumentation in AI. It offers a review of the foundational issues in argumentation and formal argument modeling.

• The History and Future of Stiquito, a Hexapod Insectoid Robot.- Learning Legged Locomotion.- Salamandra Robotica: a Biologically Inspired Amphibious Robot that Swims and Walks.- Multi-Locomotion Robot: Novel Concept, Mechanism and Control of Bio-Inspired Robot.- Self-Regulatory Hardware: Evolutionary Design for Mechanical Passivity on a Pseudo Passive Dynamic Walker.- Perception for Action in Roving Robots: a Dynamical System Approach.- Nature-Inspired Single-Electron Computers.- Tribolon: Water Based Self-Assembly Robots.- Artificial Symbiosis in Ecobots.- The Phi-Bot: A Robot Controlled by a Slime Mould.- Reaction-Diffusion Controllers for Robots.- Index.
• (source: Nielsen Book Data)

Hopping, climbing and swimming robots, nano-size neural networks, motorless walkers, slime mould and chemical brains - "Artificial Life Models in Hardware" offers unique designs and prototypes of life-like creatures in conventional hardware and hybrid bio-silicon systems. Ideas and implementations of living phenomena in non-living substrates cast a colorful picture of state-of-art advances in hardware models of artificial life.
(source: Nielsen Book Data)

• I. Introduction to dataset shift
• 1. When training and test sets are different: characterizing learning transfer / Amos Storkey
• 2. Projection and projectability / David Corfield
• II. Theoretical views on dataset and covariate shift
• 3. Binary classification under sample selection bias / Matthias Hein
• 4. On Bayesian transduction: implications for the covariate shift problem / Lars Kai Hansen
• 5. On the training/test distributions gap: a data representation learning framework / Shai Ben-David
• III. Algorithms for covariate shift
• 6. Geometry of covariate shift with applications to active learning / Takafumi Kanamori and Hidetoshi Shimodaira
• 7. A conditional expectation approach to model selection and active learning under covariate shift / Masashi Sugiyama, Neil Rubens and Klaus-Robert Muller
• 8. Covariate shift by kernel mean matching / Arthur Grellon, Alex Smola, Jiayuan Huang, Marcel Schmittfull, Karsten Borgwardt and Bernhard Scholkopf
• 9. Discriminative learning under covariate shift with a single optimization problem / Steffen Bickel, Michael Bruckner and Tobias Scheffer
• 10. An adversarial view of covariate shift and a minimax approach / Amir Globerson, Choon Hui Teo, Alex Smola and Sam Roweis
• IV. Discussion
• 11. Author comments / Hidetoshi Shimodaira, Masashi Sugiyama, Amos Storkey, Arthur Gretton and Shai-Ben David.

An overview of recent efforts in the machine learning community to deal with dataset and covariate shift, which occurs when test and training inputs and outputs have different distributions. Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is -email spam filtering, which may fail to recognize spam that differs in form from the spam the automatic filter has been built on.) Despite this, and despite the attention given to the apparently similar problems of semi-supervised learning and active learning, dataset shift has received relatively little attention in the machine learning community until recently. This volume offers an overview of current efforts to deal with dataset and covariate shift. The chapters offer a mathematical and philosophical introduction to the problem, place dataset shift in relationship to transfer learning, transduction, local learning, active learning, and semi-supervised learning, provide theoretical views of dataset and covariate shift (including decision theoretic and Bayesian perspectives), and present algorithms for covariate shift. Contributors Shai Ben-David, Steffen Bickel, Karsten Borgwardt, Michael Bruckner, David Corfield, Amir Globerson, Arthur Gretton, Lars Kai Hansen, Matthias Hein, Jiayuan Huang, Choon Hui Teo, Takafumi Kanamori, Klaus-Robert Muller, Sam Roweis, Neil Rubens, Tobias Scheffer, Marcel Schmittfull, Bernhard Schoelkopf Hidetoshi Shimodaira, Alex Smola, Amos Storkey, Masashi Sugiyama.
(source: Nielsen Book Data)

• FOUNDATIONS COMPUTATIONAL INTELLIGENCE EVOLUTIONARY COMPUTATION CONCEPTS AND PARADIGMS EVOLUTIONARY COMPUTATION IMPLEMENTATIONS NEURAL NETWORK CONCEPTS AND PARADIGMS NEURAL NETWORK IMPLEMENTATIONS FUZZY SYSTEMS CONCEPTS AND PARADIGMS FUZZY SYSTEMS IMPLEMENTATIONS COMPUTATIONAL INTELLIGENCE IMPLEMENTATIONS PERFORMANCE METRICS ANALYSIS AND EXPLANATION CASE STUDY SUMMARIES.
• (source: Nielsen Book Data)

Computational Intelligence: Concepts to Implementations provides the most complete and practical coverage of computational intelligence tools and techniques to date. This book integrates various natural and engineering disciplines to establish Computational Intelligence. This is the first comprehensive textbook on the subject, supported with lots of practical examples. It asserts that computational intelligence rests on a foundation of evolutionary computation. This refreshing view has set the book apart from other books on computational intelligence. This book lays emphasis on practical applications and computational tools, which are very useful and important for further development of the computational intelligence field. Focusing on evolutionary computation, neural networks, and fuzzy logic, the authors have constructed an approach to thinking about and working with computational intelligence that has, in their extensive experience, proved highly effective. The book moves clearly and efficiently from concepts and paradigms to algorithms and implementation techniques by focusing, in the early chapters, on the specific con. It explores a number of key themes, including self-organization, complex adaptive systems, and emergent computation. It details the metrics and analytical tools needed to assess the performance of computational intelligence tools. The book concludes with a series of case studies that illustrate a wide range of successful applications. This book will appeal to professional and academic researchers in computational intelligence applications, tool development, and systems.
(source: Nielsen Book Data)

• FOUNDATIONS COMPUTATIONAL INTELLIGENCE EVOLUTIONARY COMPUTATION CONCEPTS AND PARADIGMS EVOLUTIONARY COMPUTATION IMPLEMENTATIONS NEURAL NETWORK CONCEPTS AND PARADIGMS NEURAL NETWORK IMPLEMENTATIONS FUZZY SYSTEMS CONCEPTS AND PARADIGMS FUZZY SYSTEMS IMPLEMENTATIONS COMPUTATIONAL INTELLIGENCE IMPLEMENTATIONS PERFORMANCE METRICS ANALYSIS AND EXPLANATION CASE STUDY SUMMARIES.
• (source: Nielsen Book Data)

Computational Intelligence: Concepts to Implementations provides the most complete and practical coverage of computational intelligence tools and techniques to date. This book integrates various natural and engineering disciplines to establish Computational Intelligence. This is the first comprehensive textbook on the subject, supported with lots of practical examples. It asserts that computational intelligence rests on a foundation of evolutionary computation. This refreshing view has set the book apart from other books on computational intelligence. This book lays emphasis on practical applications and computational tools, which are very useful and important for further development of the computational intelligence field. Focusing on evolutionary computation, neural networks, and fuzzy logic, the authors have constructed an approach to thinking about and working with computational intelligence that has, in their extensive experience, proved highly effective. The book moves clearly and efficiently from concepts and paradigms to algorithms and implementation techniques by focusing, in the early chapters, on the specific con. It explores a number of key themes, including self-organization, complex adaptive systems, and emergent computation. It details the metrics and analytical tools needed to assess the performance of computational intelligence tools. The book concludes with a series of case studies that illustrate a wide range of successful applications. This book will appeal to professional and academic researchers in computational intelligence applications, tool development, and systems.
(source: Nielsen Book Data)

• Series Foreword; Preface; 1
• Introduction to Semi-Supervised Learning; 2
• A Taxonomy for Semi-Supervised Learning Methods; 3
• Semi-Supervised Text Classification Using EM; 4
• Risks of Semi-Supervised Learning: How Unlabeled Data Can Degrade Performance of Generative Classifiers; 5
• Probabilistic Semi-Supervised Clustering with Constraints; 6
• Transductive Support Vector Machines; 7
• Semi-Supervised Learning Using Semi- Definite Programming; 8
• Gaussian Processes and the Null-Category Noise Model; 9
• Entropy Regularization; 10
• Data-Dependent Regularization.

• 11
• Label Propagation and Quadratic Criterion12
• The Geometric Basis of Semi-Supervised Learning; 13
• Discrete Regularization; 14
• Semi-Supervised Learning with Conditional Harmonic Mixing; 15
• Graph Kernels by Spectral Transforms; 16- Spectral Methods for Dimensionality Reduction; 17
• Modifying Distances; 18
• Large-Scale Algorithms; 19
• Semi-Supervised Protein Classification Using Cluster Kernels; 20
• Prediction of Protein Function from Networks; 21
• Analysis of Benchmarks; 22
• An Augmented PAC Model for Semi- Supervised Learning.

• 23
• Metric-Based Approaches for Semi- Supervised Regression and Classification24
• Transductive Inference and Semi-Supervised Learning; 25
• A Discussion of Semi-Supervised Learning and Transduction; References; Notation and Symbols; Contributors; Index.

In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.Olivier Chapelle and Alexander Zien are Research Scientists and Bernhard Scholkopf is Professor and Director at the Max Planck Institute for Biological Cybernetics in Tubingen. Scholkopf is coauthor of Learning with Kernels (MIT Press, 2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by The MIT Press.
(source: Nielsen Book Data)
A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems: state-of-the-art algorithms, a taxonomy of the field, applications, benchmark experiments, and directions for future research. In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.
(source: Nielsen Book Data)

The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification-that is, a scale parameter-rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms. The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba.
(source: Nielsen Book Data)

The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification-that is, a scale parameter-rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms. The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba.
(source: Nielsen Book Data)

• Rethinking the Human-Agent Relationship: Which Social Cues Do Interactive Agents Really Need to Have? / Astrid Weiss, Manfred Tscheligi
• Believability Through Psychosocial Behaviour: Creating Bots That Are More Engaging and Entertaining / Christine Bailey, Jiaming You, Gavan Acton, Adam Rankin
• Actor Bots / Maria Arinbjarnar, Daniel Kudenko
• Embodied Conversational Agent Avatars in Virtual Worlds: Making Today's Immersive Environments More Responsive to Participants / Jacquelyn Ford Morie, Eric Chance, Kip Haynes, Dinesh Rajpurohit
• Human-Like Combat Behaviour via Multiobjective Neuroevolution / Jacob Schrum, Igor V. Karpov, Risto Miikkulainen
• Believable Bot Navigation via Playback of Human Traces / Igor V. Karpov, Jacob Schrum, Risto Miikkulainen
• A Machine Consciousness Approach to the Design of Human-Like Bots / Raúl Arrabales, Jorge Muñoz, Agapito Ledezma, German Gutierrez
• ConsScale FPS: Cognitive Integration for Improved Believability in Computer Game Bots / Raúl Arrabales, Agapito Ledezma, Araceli Sanchis
• Assessing Believability / Julian Togelius, Georgios N. Yannakakis, Sergey Karakovskiy, Noor Shaker
• Creating a Personality System for RTS Bots / Jacek Mańdziuk, Przemysław Szałaj
• Making Diplomacy Bots Individual / Markus Kemmerling, Niels Ackermann, Mike Preuss
• Towards Imitation of Human Driving Style in Car Racing Games / Jorge Muñoz, German Gutierrez, Araceli Sanchis.

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)

• Organic Computing
• A Paradigm Shift for Complex Systems; Preface; Contents; Review Team; Projects; Contributors; Chapter 1: Theoretical Foundations; Chapter 1.1: Adaptivity and Self-organisation in Organic Computing Systems; Chapter 1.2: Quantitative Emergence; Chapter 1.3: Divergence Measures as a Generalised Approach to Quantitative Emergence; Chapter 1.4: Emergent Control; Chapter 1.5: Constraining Self-organisation Through Corridors of Correct Behaviour: The Restore Invariant Approach; Chapter 1.6: Ant Inspired Methods for Organic Computing.

• Chapter 1.7: Organic Computing: Metaphor or Model?
• Chapter 2: Methods and Tools; Chapter 2.1: Model-Driven Development of Self-organising Control Applications; Chapter 2.2: How to Design and Implement Self-organising Resource-Flow Systems; Chapter 2.3: Monitoring and Self-awareness for Heterogeneous, Adaptive Computing Systems; Chapter 2.4: Generic Emergent Computing in Chip Architectures; Chapter 2.5: Multi-objective Intrinsic Evolution of Embedded Systems; Chapter 2.6: Organisation-Oriented Chemical Programming; Chapter 2.7: Hovering Data Clouds for Organic Computing;
• Chapter 3: Learning.

• Chapter 3.1: Aspects of Learning in OC SystemsChapter 3.2: Combining Software and Hardware LCS for Lightweight On-chip Learning; Chapter 3.3: Collaborative Learning by Knowledge Exchange; Chapter 3.4: A Framework for Controlled Self-optimisation in Modular System Architectures; Chapter 3.5: Increasing Learning Speed by Imitation in Multi-robot Societies; Chapter 3.6: Learning to Look at Humans;
• Chapter 4: Architectures; Chapter 4.1: Observation and Control of Organic Systems; Chapter 4.2: Organic Computing Middleware for Ubiquitous Environments.

• Chapter 4.3: DodOrg-A Self-adaptive Organic Many-core ArchitectureChapter 4.4: The Artificial Hormone System-An Organic Middleware for Self-organising Real-Time Task Allocation; Chapter 4.5: ORCA: An Organic Robot Control Architecture; Chapter 4.6: The EPOC Architecture-Enabling Evolution Under Hard Constraints; Chapter 4.7: Autonomic System on Chip Platform;
• Chapter 5: Applications; Chapter 5.1: Organic Traffic Control; Chapter 5.2: Methods for Improving the Flow of Traffic; Chapter 5.3: Applying ASoC to Multi-core Applications for Workload Management.

• Chapter 5
• .4: Efficient Adaptive Communication from Resource-Restricted TransmittersChapter 5
• .5: OrganicBus: Organic Self-organising Bus-Based Communication Systems; Chapter 5
• .6: OC Principles in Wireless Sensor Networks; Chapter 5
• .7: Application of the Organic Robot Control Architecture ORCA to the Six-Legged Walking Robot OSCAR; Chapter 5
• .8: Energy-Awareness in Self-organising Robotic Exploration Teams; Chapter 5
• .9: A Fast Hierarchical Learning Approach for Autonomous Robots; Chapter 5
• .10: Emergent Computing with Marching Pixels for Real-Time Smart Camera Applications.

Organic Computing has emerged as a challenging vision for future information processing systems. Its basis is the insight that we will increasingly be surrounded by and depend on large collections of autonomous systems, which are equipped with sensors and actuators, aware of their environment, communicating freely, and organising themselves in order to perform actions and services required by the users. These networks of intelligent systems surrounding us open fascinating application areas and at the same time bear the problem of their controllability. Hence, we have to construct such systems as robust, safe, flexible, and trustworthy as possible. In particular, a strong orientation towards human needs as opposed to a pure implementation of the technologically possible seems absolutely central. The technical systems, which can achieve these goals will have to exhibit life-like or "organic" properties. "Organic Computing Systems" adapt dynamically to their current environmental conditions. In order to cope with unexpected or undesired events they are self-organising, self-configuring, self-optimising, self-healing, self-protecting, self-explaining, and context-aware, while offering complementary interfaces for higher-level directives with respect to the desired behaviour. First steps towards adaptive and self-organising computer systems are being undertaken. Adaptivity, reconfigurability, emergence of new properties, and self-organisation are hot topics in a variety of research groups worldwide. This book summarises the results of a 6-year priority research program (SPP) of the German Research Foundation (DFG) addressing these fundamental challenges in the design of Organic Computing systems. It presents and discusses the theoretical foundations of Organic Computing, basic methods and tools, learning techniques used in this context, architectural patterns and many applications. The final outlook shows that in the mean-time Organic Computing ideas have spawned a variety of promising new projects

• Neural Networks.- Complex Functional Network Hebbian-Type Learning Algorithm and Convergence.- A New Intelligent Control Strategy of High-Voltage Power Supply for ECRH Based on CMAC Neural Network.- Self-configuration Using Artificial Neural Networks.- Evolutionary Learning and Genetic Algorithms.- An Improvement of AdaBoost for Face Detection with Random Forests.- Hybrid Good Point Set Evolutionary Strategy for Constrained Optimization.- Research of Modified Quantum Genetic Algorithm and It's Application in Collision Detection.- Granular Computing and Rough Sets.- A New Method of Attribute Reduction and Prediction in Fuzzy Decision System.- Particle Swarm Optimization and Niche Technology.- Development of Automatic Code Generation Tool for Condensation Algorithm.- Swarm Intelligence and Optimization.- A New Hybrid Multi-objective Pareto Archive PSO Algorithm for a Classic Job Shop Scheduling Problem with Ready Times.- Multi-objective Particle Swarm Optimization for Sequencing and Scheduling a Cellular Manufacturing System.- A Hybrid PSO Algorithm with Transposon for Multiobjective Optimization.- Independent Component Analysis and Blind Source Separation.- Joint Multichannel Blind Speech Separation and Dereverberation: A Real-Time Algorithmic Implementation.- An Efficient Pairwise Kurtosis Optimization Algorithm for Independent Component Analysis.- Combinatorial and Numerical Optimization.- The Mechanical Behavior of the Double Piece of Tape Spring.- Systems Biology and Computational Biology.- Cancer Immunoprevention: What Can We Learn from in Silico Models?.- Oscillatory Dynamics of Double Negative Feedback Loop Motif by MicroRNAs.- Neural Computing and Optimization.- Neural Network Approach for Greenery Warranty Systems.- Knowledge Discovery and Data Mining.- Comprehensive Evaluation of Effects of Naomaitong and Rhubarb Aglycone Combined with Bone Mesenchymal Stem Cells Transplantation on Brain in Rats with Cerebral Ischemia Based on Analytic Hierarchy Process.- Aggregating and Weighting Expert Knowledge in Group Decision Making.- A New Heuristic Feature Selection Algorithm Based on Rough Sets.- Developing a Procedure to Obtain Knowledge of Optimum Solutions in a Travelling Salesman Problem.- Trajectory Simplification and Classification for Moving Object with Road-Constraint.- Training a Pac-Man Player with Minimum Domain Knowledge and Basic Rationality.- Hybrid Self-Organizing Map and Neural Network Clustering Analysis for Technology Professionals Turnover Rate Forecasting.- Ensemble Methods.- A Study of Strength and Correlation in Random Forests.- Machine Learning Theory and Methods.- A Comparison Study of Conditional Random Fields Toolkits.- Robot Reinforcement Learning Based on Learning Classifier System.- Intelligent Computing in Bioinformatics.- Research of Marker Gene Selection for Tumor Classfication Based on Decision Forests.- Reverse Engineered Gene Networks Reveal Markers Predicting the Outcome of Breast Cancer.- New Tools for Expression Alternative Splicing Validation.- Intelligent Computing in Computational Biology and Drug Design.- Application for Artificial Bee Colony Algorithm in Migration of Mobile Agent.- Computational Genomics and Proteomics.- A Novel Tool for Assisted In-silico Cloning and Sequence Editing in Molecular Biology.- Intelligent Computing in Signal Processing.- A New Method Using Table to Sparse Representation in Pairs of Bases with Matching Pursuits.- Intelligent Computing in Pattern Recognition.- Palmprint Recognition Method Using WTA-ICA Based on 2DPCA.- Study of TCM Diagnosis of Syndromes of Acute Exacerbation of Chronic Obstructive Pulmonary Disease Based on Dynamic Fuzzy Kohonen Network.- Classification and Characteristics of TCM Syndromes of Chronic Respiratory Failure Based on Self-adaptive Fuzzy Inference System.- Implementation of the Pattern Matching System to Detect Flip Chip PCB Defects.- JPEG Steganalysis Using Estimated Image and Markov Model.- Applications of Two-Dimensional Heteroscedastic Discriminant Analysis in Face Recognition.- Intelligent Computing in Image Processing.- Multimodal Image Fusion Algorithm Using Dual-Tree Complex Wavelet Transform and Particle Swarm Optimization.- An Adaptive Method for Lane Marking Detection Based on HSI Color Model.- An Image Data Hiding Method Using Pixel-Based JND Model.- Intelligent Computing Applications Based on Eye Gaze: Their Role in Medical Image Interpretation.- Intelligent Computing in Communication and Computer Networks.- A Sequential Test Based Cooperative Spectrum Sensing Scheme Using Fuzzy Logic for Cognitive Radio Networks.- Solving Base Station Subsystem Assignment Problem in Mobile Communication Networks Using Hybridized Heuristic Algorithm.- A Sequential Cooperative Spectrum Sensing Scheme Based on Dempster Shafer Theory of Evidence.- High Resolution Direction Finding of Multiple Coherent Signals.- Extract and Maintain the Most Helpful Wavelet Coefficients for Continuous K-Nearest Neighbor Queries in Stream Processing.- A Neural Network-Based Cooperative Spectrum Sensing Scheme for Cognitive Radio Systems.- Intelligent Computing in Robotics.- Comparison of the Observability Indices for Robot Calibration considering Joint Stiffness Parameters.- Intelligent Computing in Computer Vision.- Human Computer Interaction Using Hand Gestures.- Smart Wheelchair Navigation Based on User's Gaze on Destination.- Entrance Detection of Building Component Based on Multiple Cues.- Intelligent Prediction and Time Series Analysis.- Developing an Evolutionary Neural Network Model for Stock Index Forecasting.- A Tower-Shadow Model for Wind Turbines Using a Wavelet-Prony Method.- Special Session on New Hand-Based Biometric Methods.- Contrast Enhancement and Metrics for Biometric Vein Pattern Recognition.- Feature Extraction Method for Contactless Palmprint Biometrics.- Special Session on Theories and Applications in Advanced Intelligent Computing.- Numerical Analysis for Stochastic Investment System with Poisson Jumps.- Quantum Collapsing Median Filter.- An Impulse C Application in the LDPC Decoding Algorithm.- Research on Algorithm of Parallel Garbage Collection Based on LISP 2 for Multi-core System.- Study on Anode Effect Prediction of Aluminium Reduction Applying Wavelet Packet Transform.- Rotating Machinery Fault Diagnosis Based on EMD-Approximate Entropy and LS-SVM.- Study of Applications Based on Measurement Technology in the Future Smart Grid.- Palm Line Extraction Using FRIT.- Fast ICA for Multi-speaker Recognition System.- Variable Universe Adaptive Fuzzy-PI Compound Control Applied in Maximum Power Point Tracking for Photovoltaic Energy Generation System.- Integrated and Automated Dielectric Measurement System at Millimeter Wavelengths.- Special Session on Search Based Software Engineering.- Software Security Testing of Web Applications Based on SSD.- Special Session on Bio-inspired Computing and Applications.- A Review of Bacterial Foraging Optimization Part I: Background and Development.- A Review of Bacterial Foraging Optimization Part II : Applications and Challenges.- Liquidity Risk Portfolio Optimization Using Swarm Intelligence.- Special Session on Advance in Dimensionality Reduction Methods and Its Applications.- Dimension Reduction with Semi-supervised Pairwise Covariance-Preserving Projection.- Special Session on Recent Advances in Medical Informatics.- Analysis of Impact Factors in Acupuncture for Patients with Migraine--Doubts on Prof. Andrew J Vickers' Conclusion.
• (source: Nielsen Book Data)

• Neural Networks.- Design of a Novel Six-Dimensional Force/Torque Sensor and Its Calibration Based on NN.- Incremental-Based Extreme Learning Machine Algorithms for Time-Variant Neural Networks.- Global Exponential Robust Stability of Hopfield Neural Networks with Reaction-Diffusion Terms.- Direct Inverse Model Control Based on a New Improved CMAC Neural Network.- Further Research on Extended Alternating Projection Neural Network.- Global Synchronization in an Array of Hybrid Coupling Neural Networks with Multiple Time-Delay Components.- Colour Image Segmentation Based on a Spiking Neural Network Model Inspired by the Visual System.- The White Noise Impact on the Optimal Performance of the Hopfield Neural Network.- The Study and Realization of Virtual Organization File System Based on DHT Technology.- Evolutionary Learning and Genetic Algorithms.- A Novel Quantum Genetic Algorithm for PID Controller.- Research on Hybrid Evolutionary Algorithms with Differential Evolution and GUO Tao Algorithm Based on Orthogonal Design.- An Improved Evolution Strategy for Constrained Circle Packing Problem.- Lecture Notes in Computer Science: Research on Multi-robot Avoidance Collision Planning Based on XCS.- Fuzzy Theory and Models.- An Integrated Method for the Construction of Compact Fuzzy Neural Models.- Scalarization of Type-1 Fuzzy Markov Chains.- Fuzzy Systems and Soft Computing.- Applying Fuzzy Differential Equations to the Performance Analysis of Service Composition.- Lattice Structures of Fuzzy Soft Sets.- A Predicate Formal System of Universal Logic with Projection Operator.- A Neuro-Evolutive Interval Type-2 TSK Fuzzy System for Volatile Weather Forecasting.- A Soft Computing Approach for Obtaining Transition Regions in Satellite Images.- Particle Swarm Optimization and Niche Technology.- Particle Swarm Optimization for Two-Stage Fuzzy Generalized Assignment Problem.- A Novel Cyclic Discrete Optimization Framework for Particle Swarm Optimization.- Economic Dispatch Considering Ancillary Service Based on Revised Particle Swarm Optimization Algorithm.- Particle Swarm Optimization-Based Extremum Seeking Control.- Image Contour Extraction Based on Ant Colony Algorithm and B-snake.- Supervised and Semi-supervised Learning.- An Improved Hidden Markov Model for Literature Metadata Extraction.- Discriminative Training of Subspace Gaussian Mixture Model for Pattern Classification.- Unsupervised and Reinforcement Learning.- A Stage by Stage Pruning Algorithm for Detecting the Number of Clusters in a Dataset.- Adaptive Independent Component Analysis by Modified Kernel Density Estimation.- Combinatorial and Numerical Optimization.- Cross System Bank Branch Evaluation Using Clustering and Data Envelopment Analysis.- He's Variational Iteration Method for Solving Convection Diffusion Equations.- GRASP for Low Autocorrelated Binary Sequences.- miRNA Target Prediction Method Based on the Combination of Multiple Algorithms.- Imperialistic Competitive Algorithm for Solving a Dynamic Cell Formation Problem with Production Planning.- Systems Biology and Computational Biology.- Genome-Wide DNA Methylation Profiling in 40 Breast Cancer Cell Lines.- GRIDUISS - A Grid Based Universal Immune System Simulator Framework.- Performance Comparison of Tumor Classification Based on Linear and Non-linear Dimensionality Reduction Methods.- Neural Computing and Optimization.- PH Optimal Control in the Clarifying Process of Sugar Cane Juice Based on DHP.- Nature Inspired Computing and Optimization.- Parameter-Free Deterministic Global Search with Simplified Central Force Optimization.- Comparison of Optimality and Robustness between SA, TS and GRASP Metaheuristics in FJSP Problem.- Hardware Emulation of Bacterial Quorum Sensing.- Knowledge Discovery and Data Mining.- Finding Research Community in Collaboration Network with Expertise Profiling.- The Ideal Data Representation for Feature Extraction of Traditional Malay Musical Instrument Sounds Classification.- Mining Reputation of Person/Product from Comment and Reply on UCC/Internet Article.- Interaction Analysis for Adaptive User Interfaces.- Unsupervised Subjectivity-Lexicon Generation Based on Vector Space Model for Multi-Dimensional Opinion Analysis in Blogosphere.- Enhancing Negation-Aware Sentiment Classification on Product Reviews via Multi-Unigram Feature Generation.- Building Associated Semantic Overlay for Discovering Associated Services.- Artificial Life and Artificial Immune Systems.- Immunity-Based Model for Malicious Code Detection.- Sparse Representation-Based Face Recognition for One Training Image per Person.- Semi-supervised Local Discriminant Embedding.- Orthogonal Discriminant Local Tangent Space Alignment.- Intelligent Computing in Image Processing.- Separating Pigment Components of Leaf Color Image Using FastICA.- Fast Algorithm for Multisource Image Registration Based on Geometric Feature of Corners.- Newborn Footprint Recognition Using Subspace Learning Methods.- Plant Classification Using Leaf Image Based on 2D Linear Discriminant Analysis.- Palmprint Recognition Combining LBP and Cellular Automata.- Dual Unsupervised Discriminant Projection for Face Recognition.- Applying Wikipedia-Based Explicit Semantic Analysis for Query-Biased Document Summarization.- Special Session on New Hand-Based Biometric Methods.- A New Approach for Vein Pattern-Based Recognition.- Study of Hand-Dorsa Vein Recognition.- DHV Image Registration Using Boundary Optimization.- Special Session on Recent Advances in Image Segmentation.- A Novel Level Set Model Based on Local Information.- A Multi-Descriptor, Multi-Nearest Neighbor Approach for Image Classification.- Orthogonal Locally Discriminant Projection for Palmprint Recognition.- Special Session on Theories and Applications in Advanced Intelligent Computing.- OPC UA Based Information Modeling for Distributed Industrial Systems.- Voting-Averaged Combination Method for Regressor Ensemble.- Face Recognition Using the Feature Fusion Technique Based on LNMF and NNSC Algorithms.- A PDOC Method for Topology Optimization Design.- Special Session on Search Based Software Engineering.- A Decision Support System Based on GIS for Grain Logistics Vehicle Routing Problem.- On Database Normalization Using User Interface Normal Form.- Special Session on Bio-inspired Computing and Applications.- Improved Particle Swarm Optimizers with Application on Constrained Portfolio Selection.- An Improved Image Rectification Algorithm Based on Particle Swarm Optimization.- Particle Swarm Optimizer Based on Small-World Topology and Comprehensive Learning.- Multi-Objective PSO Based on Evolutionary Programming.- Special Session on Advance in Dimensionality Reduction Methods and Its Applications.- Two-Dimensional Sparse Principal Component Analysis for Palmprint Recognition.- Discovery of Protein's Multifunction and Diversity of Information Transmission.- Special Session on Protein and Gene Bioinformatics: Methods and Applications.- Identification and Analysis of Binding Site Residues in Protein Complexes: Energy Based Approach.- Density Based Merging Search of Functional Modules in Protein-Protein Interaction (PPI) Networks.- Topology Prediction of ?-Helical and ?-Barrel Transmembrane Proteins Using RBF Networks.- Palmprint Recognition Based on Neighborhood Rough Set.- Increasing Reliability of Protein Interactome by Combining Heterogeneous Data Sources with Weighted Network Topological Metrics.- Predicting Protein Stability Change upon Double Mutation from Partial Sequence Information Using Data Mining Approach.- Inference of Gene Expression Regulation via microRNA Transfection.- A Residual Level Potential of Mean Force Based Approach to Predict Protein-Protein Interaction Affinity.
• (source: Nielsen Book Data)

The International Conference on Intelligent Computing (ICIC) was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intel- gence, machine learning, pattern recognition, image processing, bioinformatics, and computational biology. It aims to bring together researchers and practitioners from both academia and industry to share ideas, problems, and solutions related to the m- tifaceted aspects of intelligent computing. ICIC 2010, held in Changsha, China, August 18-21, 2010, constituted the 6th - ternational Conference on Intelligent Computing. It built upon the success of ICIC 2009, ICIC 2008, ICIC 2007, ICIC 2006, and ICIC 2005 that were held in Ulsan, Korea, Shanghai, Qingdao, Kunming and Hefei, China, respectively. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was "Advanced Intelligent Computing Technology and Applications". Papers focusing on this theme were solicited, addressing theories, methodologies, and applications in science and technology.
(source: Nielsen Book Data)

• Cyber System.- Cyber-Physical Systems: A New Frontier.- Security.- Misleading Learners: Co-opting Your Spam Filter.- Survey of Machine Learning Methods for Database Security.- Identifying Threats Using Graph-based Anomaly Detection.- On the Performance of Online Learning Methods for Detecting Malicious Executables.- Efficient Mining and Detection of Sequential Intrusion Patterns for Network Intrusion Detection Systems.- A Non-Intrusive Approach to Enhance Legacy Embedded Control Systems with Cyber Protection Features.- Image Encryption and Chaotic Cellular Neural Network.- Privacy.- From Data Privacy to Location Privacy.- Privacy Preserving Nearest Neighbor Search.- Reliability.- High-Confidence Compositional Reliability Assessment of SOA-Based Systems Using Machine Learning Techniques.- Model, Properties, and Applications of Context-Aware Web Services.
• (source: Nielsen Book Data)

Many networked computer systems are far too vulnerable to cyber attacks that can inhibit their functioning, corrupt important data, or expose private information. Not surprisingly, the field of cyber-based systems is a fertile ground where many tasks can be formulated as learning problems and approached in terms of machine learning algorithms. This book contains original materials by leading researchers in the area and covers applications of different machine learning methods in the reliability, security, performance, and privacy issues of cyber space. It enables readers to discover what types of learning methods are at their disposal, summarizing the state-of-the-practice in this significant area, and giving a classification of existing work.Those working in the field of cyber-based systems, including industrial managers, researchers, engineers, and graduate and senior undergraduate students will find this an indispensable guide in creating systems resistant to and tolerant of cyber attacks.
(source: Nielsen Book Data)

• Title page; Preface; Conference Organization; Contents; Invited Talks; Neural Networks; Computer Vision; Applications; Machine Learning; Reasoning; Planning and Robotics; Multiagent Systems; Author Index.

Provides a representative selection of papers promoting synergies in the research community and includes papers on: Neural Networks, Computer Vision, Applications, Machine Learning, Reasoning, Planning and Robotics, and Multi-Agent Systems. These papers are useful to those computer scientists' or engineers' interested in Artificial Intelligence.

• Preface. Acknowledgments.
• Chapter 1. Introduction. 1.1 The Machine Intelligence Research. 1.2 The Two-Fold Objectives: Data-Driven and Biologically-Inspired Approaches. 1.3 How to Read this Book. 1.4 Summary and Further Reading. References.
• Chapter 2. Incremental Learning. 2.1 Introduction. 2.2 Problem Foundation. 2.3 An Adaptive Incremental Learning Framework. 2.4 Design of the Mapping Function. 2.5 Case Study. 2.6 Summary.
• Chapter 3. Imbalanced Learning. 3.1 Introduction. 3.2 Nature of the Imbalanced Learning. 3.3 Solutions for Imbalanced Learning. 3.4 Assessment Metrics for Imbalanced Learning. 3.5 Opportunities and Challenges. 3.6 Case Study. 3.7 Summary.
• Chapter 4. Ensemble Learning. 4.1 Introduction. 4.2 Hypothesis Diversity. 4.3 Developing Multiple Hypotheses. 4.4 Integrating Multiple Hypotheses. 4.5 Case Study. 4.6 Summary.
• Chapter 5. Adaptive Dynamic Programming for Machine Intelligence. 5.1 Introduction. 5.2 Fundamental Objectives: Optimization and Prediction. 5.3 ADP for Machine Intelligence. 5.4 Case Study. 5.5 Summary.
• Chapter 6. Associative Learning. 6.1 Introduction. 6.2 Associative Learning Mechanism. 6.3 Associative Learning in Hierarchical Neural Networks. 6.4 Case Study. 6.5 Summary.
• Chapter 7. Sequence Learning. 7.1 Introduction. 7.2 Foundations for Sequence Learning. 7.3 Sequence Learning in Hierarchical Neural Structure. 7.4 Level 0: A Modified Hebbian Learning Architecture. 7.5 Level 1 to Level N: Sequence Storage, Prediction and Retrieval. 7.6 Memory Requirement. 7.7 Learning and Anticipation of Multiple Sequences. 7.8 Case Study. 7.9 Summary.
• Chapter 8. Hardware Design for Machine Intelligence. 8.1 A Final Comment. References.
• (source: Nielsen Book Data)

This book will advance the understanding and application of self-adaptive intelligent systems; therefore it will potentially benefit the long-term goal of replicating certain levels of brain-like intelligence in complex and networked engineering systems. It will provide new approaches for adaptive systems within uncertain environments. This willprovide an opportunity to evaluate the strengths and weaknesses of the current state-of-the-art of knowledge, give rise to new research directions, and educate future professionals in this domain. Self-adaptive intelligent systems have wide applications from military security systems to civilian daily life. In this book, different application problems, including pattern recognition, classification, image recovery, and sequence learning, will be presented to show the capability of the proposed systems in learning, memory, and prediction. Therefore, this book will also provide potential new solutions to many real-world applications.
(source: Nielsen Book Data)

• Local Ordinal Classification.- Using Genetic Algorithms and Decision Trees for a posteriori Analysis and Evaluation of Tutoring Practices based on Student Failure Models.- Exploiting Decision Trees in Product-based Fuzzy Neural Modeling to Generate Rules with Dynamically Reduced Dimensionality.- Retraining the Neural Network for Data Visualization.- Rule-Based Adaptive Navigation for an Intelligent Educational Mobile Robot.- BRWM: A relevance feedback mechanism for web page clustering.- Bagged Averaging of Regression Models.- Argument-based User Support Systems using Defeasible Logic Programming.- Knowledge Modelling Using The UML Profile.- Optimized Multi-Domain Secure Interoperation using Soft Constraints.- Learning context models for the recognition of scenarios.- MDA-Based Architecture of a Description Logics Reasoner.- Incremental guideline formalization with tool support.- Accessing data in the semantic web: An intelligent data integration and navigation approaches.- An Expert System Delivery Environment for the World Wide Web.- A Formally Specified Ontology Management API as a Registry for Ubiquitous Computing Systems.- Intelligent Configurable Electronic Shop Platform based on Ontologies and 3D Visualization.- Adapting User Interfaces to the User Interaction Requirements in Real Time.- On supporting young students in visual logic modeling.- Attentional Model for Perceiving Social Context in Intelligent Environments.- Penguin Quart - Slovak Digit Speech Recognition Game Based on HMM.- Impact of Face Registration Errors on Recognition.- Unsupervised Segmentation of Meeting Configurations and Activities using Speech Activity Detection.- A Model of Real-Time Indoor Surveillance System using Behavior Detection.- A Filter Module Used in Pedestrian Detection System.- User Localization for Intelligent Crisis Management.- An Intrusion Detection System for Network-Initiated Attacks Using a Hybrid Neural Network.- Which Adequate Trust Model for Trust Networks?.- XML Systems for Intelligent Management of Pervasive Computing Resources.- A constraint based approach for aiding heat treatment operation design and distortion evaluation.- Applying AI to Cooperating Agricultural Robots.- Capacity Evaluation of an Indoor Smart Antenna System at 60 GHz.- Steady State Contingency analysis of electrical networks using machine learning techniques.- Robust Multimodal Audio-Visual Processing for Advanced Context Awareness in Smart Spaces.- Toward supporting group dynamics.- Multimodal Integration of Sensor Network.- Multimodal Identity Tracking in a Smartroom.- Multimodal Focus Attention and Stress Detection and feedback in an Augmented Driver Simulator.- A Fuzzy Expert System for the Early Warning of Accidents Due to Driver Hypo-Vigilance.- Mixed Reality Cane Simulation.- 3D content-based search using sketches.- Manual Annotation and Automatic Image Processing of Multimodal Emotional Behaviors in TV Interviews.- MPEG-4 Facial Expression Synthesis based on Appraisal Theory.- Towards On- and Off-line Search, Browse and Replay of Home Activities.- Engineering an interoperable adaptive hypermedia testing tool supporting user adaptable strategies.- Teaching a Computer Science Course using Adaptable Instructional Images.- e-Class Personalized: Design and Evaluation of an Adaptive Learning Content Management System.- The Use of Psychophysiological Measures for Designing Adaptive Learning Systems.- Developing Personalized E-Books: A Multi-Layered Approach.- Designing a Solver for Arithmetic Constraints to Support Education in Mathematics.- A Tutoring System Discovering Gaps in the Current Body of Students' Knowledge.- Sequencing Parametric Exercises for an Operating System Course.- A gene expression analysis system for medical diagnosis.- Recording, Monitoring and Interrelating Changes of Invivo Bio-cells from Video (Biosignatures).- An Archetype for MRI guided Tele-interventions.- Differential Evolution Algorithms for Finding Predictive Gene Subsets in Microarray Data.- Feature Selection for Microarray Data Analysis Using Mutual Information and Rough Set Theory.- A Support Vector Machine Approach to Breast Cancer Diagnosis and Prognosis.- Source Code Author Identification Based on N-gram Author Profiles.- AJA - Tool for Programming Adaptable Agents.- Investigating the Predictability of Empirical Software Failure Data with Artificial Neural Networks and Hybrid Models.- Selecting the Appropriate Machine Learning Techniques for the Prediction of Software Development Costs.- On the Idea of Using Nature-Inspired Metaphors to Improve Software Testing.- Fast Video Object Tracking using Affine Invariant Normalization.- Knowledge Acquisition from Multimedia Content using an Evolution Framework.- Exploratory Search: Image Retrieval without Deep Semantics.- Word Senses: The Stepping Stones in Semantic-Based Natural Language Processing.- Space-Time Tubes and Motion Representation.- Semantic Concept Detection from News Videos with Self-Organizing Maps.- Analysis of Semantic Information Available in an Image Collection Augmented with Auxiliary Data.- Supporting Semi-Automatic Semantic Annotation of Multimedia Resources.- A Simulation Tool for Modelling Pedestrian Dynamics during Evacuation of Large Areas.- Radar Imaging by Range Density Functions.- A Method for Incremental Data Fusion in Distributed Sensor Networks.- Information Society: the two faces of Janus.- Ant Seeker: An algorithm for enhanced web search.- Increasing Intelligent Wireless Sensor Networks Survivability by Applying Energy-Efficient Schemes.- A Review of Video Watermarking and a Benchmarking Framework.- Chaotic Encryption Driven Watermarking of Human Video Objects Based on Hu Moments.- Semi-Fragile Watermarking Authentication with Local and Global Watermarks.- Decentralising the Digital Rights Management Value Chain by means of Distributed License Catalogues.- AXMEDIS architectural solution for interoperable content and DRM on multichannel distribution.- Computer Aided Diagnosis of CT Focal Liver Lesions based on Texture Features, Feature Selection and Ensembles of Classifiers.- Texture Analysis for Classification of Endometrial Tissue in Gray Scale Transvaginal Ultrasonography.- Wavelet-based Feature Analysis for Classification of Breast Masses from Normal Dense Tissue.- Microcalcification Features Extracted from Principal Component Analysis in the Wavelet Domain.- Classification of Atherosclerotic Carotid Plaques Using Gray Level Morphological Analysis on Ultrasound images.
• (source: Nielsen Book Data)

Artificial Intelligence applications build on a rich and proven theoretical background to provide solutions to a wide range of real life problems. The ever expanding abundance of information and computing power enables researchers and users to tackle higly interesting issues for the first time, such as applications providing personalized access and interactivity to multimodal information based on preferences and semantic concepts or human-machine interface systems utilizing information on the affective state of the user. The purpose of the 3rd IFIP Conference on Artificial Intelligence Applications and Innovations (AIAI) is to bring together researchers, engineers, and practitioners interested in the technical advances and business and industrial applications of intelligent systems. AIAI 2006 is focused on providing insights on how AI can be implemented in real world applications.
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• Title page; Preface; Conference Organization; Contents; Full Papers; Cognitive Learning with Automatic Goal Acquisition; Semantics of Alan; A Compact Argumentation System for Agent System Specification; Social Responsibility Among Deliberative Agents; Knowledge Base Extraction for Fuzzy Diagnosis of Mental Retardation Level; Tuning the Feature Space for Content-Based Music Retrieval; Personalizing Trust in Online Auctions; An Hybrid Soft Computing Approach for Automated Computer Design; FUNEUS: A Neurofuzzy Approach Based on Fuzzy Adaline Neurons; Empirical Evaluation of Scoring Methods

• Binarization Algorithms for Approximate Updating in Credal NetsOn Generalizing the AGM Postulates; The Two-Variable Situation Calculus; Base Belief Change and Optimized Recovery; Unsupervised Word Sense Disambiguation Using the WWW; Relational Descriptive Analysis of Gene Expression Data; Solving Fuzzy PERT Using Gradual Real Numbers; Approaches to Efficient Resource-Constrained Project Rescheduling; A Comparison of Web Service Interface Similarity Measures; Finding Alternatives Web Services to Parry Breakdowns; Posters; Smart Ride Seeker Introductory Plan

• Spam Filtering: The Influence of the Temporal Distribution of Training DataAn Approach for Evaluating User Model Data in an Interoperability Scenario; On the Improvement of Brain Tumour Data Clustering Using Class Information; Endowing BDI Agents with Capability for Modularizing; Rational Agents Under ASP in Games Theory; Automatic Generation of Natural Language Parsers from Declarative Specifications; Reconsideration on Non-Linear Base Orderings; Dynamic Abstraction for Hierarchical Problem Solving and Execution in Stochastic Dynamic Environments

• A Comparison of Two Machine-Learning Techniques to Focus the Diagnosis TaskArgumentation Semantics for Temporal Defeasible Logic; NEWPAR: An Optimized Feature Selection and Weighting Schema for Category Ranking; Challenges and Solutions for Hierarchical Task Network Planning in E-Learning; Invited Talks; Artificial Intelligence and Unmanned Aerial Vehicles; Writing a Good Grant Proposal; Author Index

STAIRS 2006 is the third European Starting AI Researcher Symposium, an international meeting aimed at AI researchers, from all countries, at the beginning of their career: PhD students or people holding a PhD for less than one year. The topics of the papers included range from traditional AI areas to AI applications, such as Agents, Automated Reasoning, Belief Revision, Case-based Reasoning, Constraints, Data Mining & Information Extraction, Genetic Algorithms, Human Computer Interaction, Interactive Sensry Systems (Speech, Multi-Model Processing), Knowledge Representation, Logic Programming, Machine Learning, Natural Language Processing, Neural Networks, Nonmonotonic Reasoning, Planning & Scheduling, Reasoning about Action and Change, Robotics, Search, Smantic Web, Spatial & Temporal Reasoning and Uncertainty.

The book includes all the background material required to understand the principles underlying intelligence, as well as enough detailed information on intelligent robotics and simulated agents so readers can begin experiments and projects on their own. By the mid-1980s researchers from artificial intelligence, computer science, brain and cognitive science, and psychology realized that the idea of computers as intelligent machines was inappropriate. The brain does not run "programs"; it does something entirely different. But what? Evolutionary theory says that the brain has evolved not to do mathematical proofs but to control our behavior, to ensure our survival. Researchers now agree that intelligence always manifests itself in behavior-thus it is behavior that we must understand. An exciting new field has grown around the study of behavior-based intelligence, also known as embodied cognitive science, "new AI, " and "behavior-based AI." This book provides a systematic introduction to this new way of thinking. After discussing concepts and approaches such as subsumption architecture, Braitenberg vehicles, evolutionary robotics, artificial life, self-organization, and learning, the authors derive a set of principles and a coherent framework for the study of naturally and artificially intelligent systems, or autonomous agents. This framework is based on a synthetic methodology whose goal is understanding by designing and building. The book includes all the background material required to understand the principles underlying intelligence, as well as enough detailed information on intelligent robotics and simulated agents so readers can begin experiments and projects on their own. The reader is guided through a series of case studies that illustrate the design principles of embodied cognitive science.
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• A Prescriptive Analytical Logic Model Incorporated with Analytic Hierarchy Process for Software Application Error Analysis
• Simplified Framework of Natural Language Processing for Structure Management of Current-Age Data
• Design and Software Implementation of Heuristic and Suboptimal Strategies for the Mancala/Kalah Game
• Sector-Selective Hybrid Scheme Facilitating Hardware Supportability Over Image Compression
• Performance Evaluation of Joint Rate-Distortion Model of Video Codec
• To Defeat DDoS Attacks in Cloud Computing Environment Using Software Defined Networking (SDN)
• Oriented Petri Nets as Means of Describing a Human-Computer Interaction
• FPGA Based Transient Fault Generate and Fault Tolerant for Asynchronous and Synchronous Circuits
• Designing an Energy Efficient Routing for Subsystems Sensors in Internet of Things Eco-System Using Distributed Approach
• The Compare of Solo Programming and Pair Programming Strategies in a Scrum Team: A Multi-agent Simulation
• Fair Random Access with Track-Based SNR Scheduling for Full-Duplex Wireless Powered Communication Networks
• A DTN Gateway-Based Architecture for Web Access in Space Internet
• Multi-objective Scheduling Optimization for ETL Tasks in Cluster Environment
• An LSTM-Based Encoder-Decoder Model for State-of-Charge Estimation of Lithium-Ion Batteries
• Analysis of Social Engineering Attacks Using Exploit Kits
• Bipolar Disorder: A Pathway Towards Research Progress in Identification and Classification
• Managing Electronic Waste with Recycling: A Review of Developing and Developed Regions
• Trajectory Modeling in a Pursuit Problem with Curvature Restrictions
• An Approach to Using Templates for Modeling Exceptions in Terms of Petri Nets
• Performance Bottleneck Analysis and Optimization Framework in Concurrent Environments
• Matlab Code Generation and Consumption
• The Problem of Preventing the Development of Critical Combinations of Events in Large-Scale Systems
• Using Software Package "Multimeat-Expert" for Modeling and Optimization of Composition Chopped Meat Product with Vegetable Additive
• Application of Machine Learning for Document Classification and Processing in Adaptive Information Systems
• SNR-Based TDMA Scheduling with Continuous Energy Transfer for Wireless Powered Communication Networks
• Comparative Analysis of Software Development Life Cycle Models (SDLC). Plus 27 more papers.

This book gathers the refereed proceedings of the Intelligent Algorithms in Software Engineering Section of the 9th Computer Science On-line Conference 2020 (CSOC 2020), held on-line in April 2020. Software engineering research and its applications to intelligent algorithms have now assumed an essential role in computer science research. In this book, modern research methods, together with applications of machine and statistical learning in software engineering research, are presented. .
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• Chapter 1. Contraction methods for correlation clustering: the order is important (Laszlo Aszalos).-
• Chapter 2. Optimisation of Preparedness and Response of Health services in major crises using the IMPRESS platform (Nina Dobrinkova).-
• Chapter 3. The new approach for dynamic optimization with variability constraints (Pawel Drag).-
• Chapter 4. Intercriteria Analisys of ACO Performance for Workforce Planning Problem (Olympia Roeva).-
• Chapter 5. Is Prufer Code encoding always a bad idea? (Hanno Hildmann).-
• Chapter 6. A model for wireless-access network topology and a PSO-based approach for its optimization (Hanno Hildmann).-
• Chapter 7. InterCriteria Analysis Approach for Comparison of Simple and Multi-population Genetic Algorithms Performance (Maria Angelova).-
• Chapter 8. Structure Optimization and Learning of Fuzzy Cognitive Map with the use of Evolutionary Algorithm and Graph Theory Metrics (Katarzyna Poczeta).-
• Chapter 9. Fuzziness in the Berth Allocation Problem (Flabio Gutierrez).-
• Chapter 10. Identifying Clusters in Spatial Data via Sequential Importance Sampling (Nishanthi Raveendran).-
• Chapter 11. Multiobjective Optimization Grover Adaptive Search (Benjamin Baran.-
• Chapter 12. Discovering Knowledge from Predominantly Repetitive Data by InterCriteria Analysis (Olympia Roeva).
• (source: Nielsen Book Data)

Our everyday lives are practically unthinkable without optimization. We constantly try to minimize our effort and to maximize the reward or progress achieved. Many real-world and industrial problems arising in engineering, economics, medicine and other domains can be formulated as optimization tasks. This volume presents a comprehensive collection of extended contributions from the 2017 Workshop on Computational Optimization. Presenting recent advances in computational optimization, it addresses important concrete applications, e.g. the modeling of physical processes, wildfire modeling, modeling processes in chemical engineering, workforce planning, wireless access network topology, parameter settings for controlling various processes, berth allocation, identification of homogeneous domains, and quantum computing. The book shows how to develop algorithms for them based on new intelligent methods like evolutionary computations, ant colony optimization, constrain programming and others.
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• Table of Contents Recognizing traffic signs using Convnets Annotating Images with Object Detection API Caption generation for images Building GANs for Conditional Image Creation Stock Price Prediction with LSTM Create & Train Machine Translation Systems Train and set up a Chatbot, able to discuss like a human Detecting Duplicate Quora Questions Building a TensorFlow Recommender Systems Video Games by Reinforcement learning.
• (source: Nielsen Book Data)

Leverage the power of Tensorflow to design deep learning systems for a variety of real-world scenarios Key Features Build efficient deep learning pipelines using the popular Tensorflow framework Train neural networks such as ConvNets, generative models, and LSTMs Includes projects related to Computer Vision, stock prediction, chatbots and more Book DescriptionTensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with very high accuracy. This book is your guide to master deep learning with TensorFlow with the help of 10 real-world projects. TensorFlow Deep Learning Projects starts with setting up the right TensorFlow environment for deep learning. Learn to train different types of deep learning models using TensorFlow, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Generative Adversarial Networks. While doing so, you will build end-to-end deep learning solutions to tackle different real-world problems in image processing, recommendation systems, stock prediction, and building chatbots, to name a few. You will also develop systems that perform machine translation, and use reinforcement learning techniques to play games. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow, and will be able to build and train your own deep learning models with TensorFlow confidently. What you will learn Set up the TensorFlow environment for deep learning Construct your own ConvNets for effective image processing Use LSTMs for image caption generation Forecast stock prediction accurately with an LSTM architecture Learn what semantic matching is by detecting duplicate Quora questions Set up an AWS instance with TensorFlow to train GANs Train and set up a chatbot to understand and interpret human input Build an AI capable of playing a video game by itself -and win it! Who this book is forThis book is for data scientists, machine learning developers as well as deep learning practitioners, who want to build interesting deep learning projects that leverage the power of Tensorflow. Some understanding of machine learning and deep learning, and familiarity with the TensorFlow framework is all you need to get started with this book.
(source: Nielsen Book Data)

• Part I Computational Models and Knowledge Discovery
• Part II Communications, Networks, and Cloud Computing
• Part III Computer-Based Systems
• Part IV Data-Oriented and Software-Intensive Systems.

This carefully edited and reviewed volume addresses the increasingly popular demand for seeking more clarity in the data that we are immersed in. It offers excellent examples of the intelligent ubiquitous computation, as well as recent advances in systems engineering and informatics. The content represents state-of-the-art foundations for researchers in the domain of modern computation, computer science, system engineering and networking, with many examples that are set in industrial application context. The book includes the carefully selected best contributions to APCASE 2014, the 2nd Asia-Pacific Conference on Computer Aided System Engineering, held February 10-12, 2014 in South Kuta, Bali, Indonesia. The book consists of four main parts that cover data-oriented engineering science research in a wide range of applications: computational models and knowledge discovery; communications networks and cloud computing; computer-based systems; and data-oriented and software-intensive systems.

• Deep Learning of Representations / Yoshua Bengio, Aaron Courville
• Recurrent Neural Networks / Sajid A. Marhon, Christopher J.F. Cameron
• Supervised Neural Network Models for Processing Graphs / Monica Bianchini, Marco Maggini
• Topics on Cellular Neural Networks / Liviu Goraş, Ion Vornicu, Paul Ungureanu
• Approximating Multivariable Functions by Feedforward Neural Nets / Paul C. Kainen, Věra Kůrková
• Bochner Integrals and Neural Networks / Paul C. Kainen, Andrew Vogt
• Semi-supervised Learning / Mohamed Farouk Abdel Hady, Friedhelm Schwenker
• Statistical Relational Learning / Hendrik Blockeel
• Kernel Methods for Structured Data / Andrea Passerini
• Multiple Classifier Systems: Theory, Applications and Tools / Francesco Gargiulo, Claudio Mazzariello
• Self Organisation and Modal Learning: Algorithms and Applications / Dominic Palmer-Brown, Chrisina Jayne
• Bayesian Networks, Introduction and Practical Applications / Wim Wiegerinck, Willem Burgers, Bert Kappen
• Relevance Feedback in Content-Based Image Retrieval: A Survey / Jing Li, Nigel M. Allinson
• Learning Structural Representations of Text Documents in Large Document Collections / Ah Chung Tsoi, Markus Hagenbuchner, Milly Kc
• Neural Networks in Bioinformatics / Masood Zamani, Stefan C. Kremer.

This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include: Deep architectures Recurrent, recursive, and graph neural networks Cellular neural networks Bayesian networks Approximation capabilities of neural networks Semi-supervised learning Statistical relational learning Kernel methods for structured data Multiple classifier systems Self organisation and modal learning Applications to content-based image retrieval, text mining in large document collections, and bioinformat.

• Stochastic Algorithms for 3D Node Localization in Anisotropic Wireless Sensor Networks
• An Evaluation of Classification Algorithms Using Mc Nemar's Test
• Permitting Features in P Systems Generating Picture Arrays
• An ACO Framework for Single Track Railway Scheduling Problem
• Bio-Inspired Soft-Computational Framework for Speech and Image Application
• Leukocyte Classification in Skin Tissue Images
• Solving Application Oriented Graph Theoretical Problems with DNA Computing
• Human Identification Using Heartbeat Interval Features and ECG Morphology
• Improved Real-Time Discretize Network Intrusion Detection System
• Identification and Impact Assessment of High-Priority Field Failures in Passenger Vehicles Using Evolutionary Optimization
• Automatic Agricultural Leaves Recognition System
• Non-Uniform Mapping in Binary-Coded Genetic Algorithms.

• Control Words of Transition P Systems
• Iso-Array Splicing Grammar System
• GA based Dimension Reduction for enhancing performance of k-Means and Fuzzy k-Means: A Case Study for Categorization of Medical Dataset
• A Computational Intelligence based Approach to Telecom Customer Classification for Value Added Services
• An Efficient Approach on Rare Association Rule Mining
• A Hybrid Multiobjective Particle Swarm Optimization Approach for Non-redundant Gene Marker Selection
• Application of High Quality Amino Acid Indices to AMS 3.0: A Update Note
• Constructive Solid Geometry Based Topology Optimization Using Evolutionary Algorithm
• Array P Systems with Hybrid Teams
• An Approach for the Ordering of Evaluation of Objectives in Multiobjective Optimization
• Extended Forma: Analysis and an Operator Exploiting it
• Incorporating Great Deluge with Harmony Search for Global Optimization Problems.

• Boundary Handling Approaches in Particle Swarm Optimization
• Diversity Measures in Artificial Bee Colony
• Digital Video Watermarking Using Scene Detection
• Self Adaptive Acceleration Factor in Particle Swarm Optimization
• Applying Case Based Reasoning in Cuckoo Search for the Expedition of Groundwater Exploration
• Reversible OR Logic Gate Design Using DNA
• Performance Enhanced Hybrid Artificial Neural Network for Abnormal Retinal Image Classification
• Algorithmic Tile Self-assembly Model for the Minimum Dominating Set Problem
• Semantic Sub-tree Crossover Operator for Postfix Genetic Programming
• Exploration Enhanced Particle Swarm Optimization using Guided Re-Initialization
• Using Firefly Algorithm to Solve Resource Constrained Project Scheduling Problem
• Analysis of Cellular Automata and Genetic Algorithm based Test Pattern Generators for Built In Self Test
• Ant Colony-based System for Retinal Blood Vessels Segmentation.

• An Efficient Neural Network Based Background Subtraction Method
• JustThink: Smart BCI Applications
• Interpretability Issues in Evolutionary Multi-Objective Fuzzy Knowledge Base Systems
• Hybrid Firefly Based Simultaneous Gene Selection and Cancer Classification Using Support Vector Machines and Random Forests
• Recognition of Online Handwritten Gurmukhi Strokes Using Support Vector Machine
• An Optimal Fuzzy Logic Controller Tuned with Artificial Immune System
• Comparisons of Different Feature Sets for Predicting Carbohydrate-Binding Proteins From Amino Acid Sequences Using Support Vector Machine
• A PSO Based Smart Unit Commitment Strategy for Power Systems Including Solar Energy
• A User-Oriented Content Based Recommender System Based on Reclusive Methods and Interactive Genetic Algorithm.

The book is a collection of high quality peer reviewed research papers presented in Seventh International Conference on Bio-Inspired Computing (BIC-TA 2012) held at ABV-IIITM Gwalior, India. These research papers provide the latest developments in the broad area of "Computational Intelligence". The book discusses wide variety of industrial, engineering and scientific applications of nature/bio-inspired computing and presents invited papers from the inventors/originators of novel computational techniques