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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)