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• Cover; Title; EUL; Contents; Foreword; Preface; List of Contributors; Keywords; Chapter 01; Chapter 02; Chapter 03; Chapter 04; Chapter 05; Chapter 06; Chapter 07; Chapter 08; Chapter 09; Chapter 10; Chapter 11; Index.

This book dives into the heart of how to design distributed control architectures for heterogeneous teams of humans, robots, and automated systems, enabling them to achieve greater cooperation and autonomy through the use of network technologiesReaders can envision a realistic view of what can be expected from a networked human robot cooperative environment in the next decade.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

• 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)

• Table of Contents An Overview of Amazon EMR Exploring the Architecture and Deployment Options Common Use Cases and Architecture Patterns Big Data Applications and Notebooks Available in Amazon EMR Setting Up and Configuring EMR Clusters Monitoring, Scaling, and High Availability Understanding Security in Amazon EMR Understanding Data Governance in Amazon EMR Implementing Batch ETL Pipeline with Amazon EMR and Apache Spark Implementing Real-Time Streaming with Amazon EMR and Spark Streaming Implementing UPSERT on S3 Data Lake with Apache Spark and Apache Hudi Orchestrating Amazon EMR Jobs with AWS Step Functions and Apache Airflow/MWAA Migrating On-Premises Hadoop Workloads to Amazon EMR Best Practices and Cost Optimization Techniques.
• (source: Nielsen Book Data)

Design scalable big data solutions using Hadoop, Spark, and AWS cloud native services Key Features Build data pipelines that require distributed processing capabilities on a large volume of data Discover the security features of EMR such as data protection and granular permission management Explore best practices and optimization techniques for building data analytics solutions in Amazon EMR Book DescriptionAmazon EMR, formerly Amazon Elastic MapReduce, provides a managed Hadoop cluster in Amazon Web Services (AWS) that you can use to implement batch or streaming data pipelines. By gaining expertise in Amazon EMR, you can design and implement data analytics pipelines with persistent or transient EMR clusters in AWS. This book is a practical guide to Amazon EMR for building data pipelines. You'll start by understanding the Amazon EMR architecture, cluster nodes, features, and deployment options, along with their pricing. Next, the book covers the various big data applications that EMR supports. You'll then focus on the advanced configuration of EMR applications, hardware, networking, security, troubleshooting, logging, and the different SDKs and APIs it provides. Later chapters will show you how to implement common Amazon EMR use cases, including batch ETL with Spark, real-time streaming with Spark Streaming, and handling UPSERT in S3 Data Lake with Apache Hudi. Finally, you'll orchestrate your EMR jobs and strategize on-premises Hadoop cluster migration to EMR. In addition to this, you'll explore best practices and cost optimization techniques while implementing your data analytics pipeline in EMR. By the end of this book, you'll be able to build and deploy Hadoop- or Spark-based apps on Amazon EMR and also migrate your existing on-premises Hadoop workloads to AWS. What you will learn Explore Amazon EMR features, architecture, Hadoop interfaces, and EMR Studio Configure, deploy, and orchestrate Hadoop or Spark jobs in production Implement the security, data governance, and monitoring capabilities of EMR Build applications for batch and real-time streaming data analytics solutions Perform interactive development with a persistent EMR cluster and Notebook Orchestrate an EMR Spark job using AWS Step Functions and Apache Airflow Who this book is forThis book is for data engineers, data analysts, data scientists, and solution architects who are interested in building data analytics solutions with the Hadoop ecosystem services and Amazon EMR. Prior experience in either Python programming, Scala, or the Java programming language and a basic understanding of Hadoop and AWS will help you make the most out of this book.
(source: Nielsen Book Data)