%{search_type} search results

• An Ontological Perspective of Web Services.- Hints from A Molecular Analogy.- Web Service Mining Framework.- Application: Biological Pathway Discovery.- Related Work.- Conclusions.
• (source: Nielsen Book Data)

The new computing environment enabled by advances in service oriented arc- tectures, mashups, and cloud computing will consist of service spaces comprising data, applications, infrastructure resources distributed over the Web. This envir- ment embraces a holistic paradigm in which users, services, and resources establish on-demand interactions, possibly in real-time, to realise useful experiences. Such interactions obtain relevant services that are targeted to the time and place of the user requesting the service and to the device used to access it. The bene?t of such environment originates from the added value generated by the possible interactions in a large scale rather than by the capabilities of its individual components se- rately. This offers tremendous automation opportunities in a variety of application domains including execution of forecasting, of?ce tasks, travel support, intelligent information gathering and analysis, environment monitoring, healthcare, e-business, community based systems, e-science and e-government. A key feature of this environment is the ability to dynamically compose services to realise user tasks. While recent advances in service discovery, composition and Semantic Web technologies contribute necessary ?rst steps to facilitate this task, the bene?ts of composition are still limited to take advantages of large-scale ubiq- tous environments. The main stream composition techniques and technologies rely on human understanding and manual programming to compose and aggregate s- vices. Recent advances improve composition by leveraging search technologies and ?ow-based composition languages as in mashups and process-centric service c- position.
(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)

• Introduction Rational Agents Strategic Games Coordination Partial Observability Mechanism Design Learning.
• (source: Nielsen Book Data)

Multiagent systems is an expanding field that blends classical fields like game theory and decentralized control with modern fields like computer science and machine learning. This monograph provides a concise introduction to the subject, covering the theoretical foundations as well as more recent developments in a coherent and readable manner. The text is centered on the concept of an agent as decision maker. Chapter 1 is a short introduction to the field of multiagent systems. Chapter 2 covers the basic theory of singleagent decision making under uncertainty. Chapter 3 is a brief introduction to game theory, explaining classical concepts like Nash equilibrium. Chapter 4 deals with the fundamental problem of coordinating a team of collaborative agents. Chapter 5 studies the problem of multiagent reasoning and decision making under partial observability. Chapter 6 focuses on the design of protocols that are stable against manipulations by self-interested agents. Chapter 7 provides a short introduction to the rapidly expanding field of multiagent reinforcement learning. The material can be used for teaching a half-semester course on multiagent systems covering, roughly, one chapter per lecture.
(source: Nielsen Book Data)

• Frequent and Closed Sequence Patterns.- Classification, Clustering, Features and Distances of Sequence Data.- Sequence Motifs: Identifying and Characterizing Sequence Families.- Mining Partial Orders from Sequences.- Distinguishing Sequence Patterns.- Related Topics.
• (source: Nielsen Book Data)

Understanding sequence data, and the ability to utilize this hidden knowledge, creates a significant impact on many aspects of our society. Examples of sequence data include DNA, protein, customer purchase history, web surfing history, and more. Sequence Data Mining provides balanced coverage of the existing results on sequence data mining, as well as pattern types and associated pattern mining methods. While there are several books on data mining and sequence data analysis, currently there are no books that balance both of these topics. This professional volume fills in the gap, allowing readers to access state-of-the-art results in one place. Sequence Data Mining is designed for professionals working in bioinformatics, genomics, web services, and financial data analysis. This book is also suitable for advanced-level students in computer science and bioengineering. Forward by Professor Jiawei Han, University of Illinois at Urbana-Champaign.
(source: Nielsen Book Data)
Understanding sequence data, and the ability to utilize this hidden knowledge, will create a significant impact on many aspects of our society. Examples of sequence data include DNA, protein, customer purchase history, web surfing history, and more. This book provides thorough coverage of the existing results on sequence data mining as well as pattern types and associated pattern mining methods. It offers balanced coverage on data mining and sequence data analysis, allowing readers to access the state-of-the-art results in one place.
(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)

• Cortical images, self-organising neural networks and object classification / Nikolay Petkov
• Parallel implementation of a neural network ensemble on the connection machine / Daijin Kim, Minsoo Suk
• Boolean neural networks trained with simulated annealing / Jarkko Niittylahti
• On the computational complexity of analyzing the Hopfield-Clique network / Arun Jagota
• A harmony-maximisation network implementation of a compound labeling scheme for scene analysis / Tatiana Tambouratzis
• Optimal image boundary via Hopfield net and tunneling / William Cheung, Roland Chin, Tong Lee
• Shape matching based on invariants / Stan Z. Li.

• Intro
• Preface
• Invited Speakers
• DSS, Classifications, Trends and Enabling Modern Information and Communication Technologies
• Distorted Statistics based on Choquet Calculus
• Overview of QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm
• Nature-Inspired Optimization of Type-2 Fuzzy Logic Controllers
• Seeing Is Believing
• Contents
• Short CVs of Guest Editors
• I Soft Computing and Conventional Techniques in Power Engineering Methods and Applications in Electrical Engineering I

• Daily Load Curve Forecasting. Comparative Analysis: Conventional vs. Unconventional Methods
• Abstract
• 1 Introduction
• 2 Developed Algorithms
• 2.1 Artificial Neural Networks (ANN)
• 2.2 Multiple Linear Regression (MLR)
• 2.3 Curve Fitting (CF)
• 2.4 Decision Trees (DT)
• 3 Numerical Results. Discussions
• 3.1 Forecast Performed for 2017 Year
• 3.2 Forecast Performed for 2018 Year
• 3.3 Comparative Analysis for Several Forecasts
• 4 Conclusion
• Acknowledgment
• References
• LoRaWAN Based Airport Runway Lights Monitoring System
• Abstract
• 1 Introduction
• 2 LoRa Technology

• 3 Case Study and Results
• 4 Conclusions
• Acknowledgment
• References
• The Enhanced Crow Search Algorithm for Fuel-Cost Function Parameters Assessment of the Cogeneration Units from Thermal Power Plants
• Abstract
• 1 Introduction
• 2 Estimating the Fuel-Cost Function Parameters
• 3 The Crow Search Algorithm (CS)
• 4 The Enhanced CS Algorithm (ECS)
• 5 Case Studies and Results
• 6 Conclusions
• References
• Modelling a Photovoltaic Power Station
• Abstract
• 1 Introduction
• 2 Simulation of the Photovoltaic Panels Using MatLAB Software

• 2.1 Simulink: Modelling of a PV Solar Cell
• 2.2 The Intensity of the Induced Electrical Current by the Sun
• 2.3 Dependence on the Temperature
• 3 The Simulation of a PV Power Station
• 4 Conclusions
• References
• Identification of Fault Locations on Tree Structure Systems
• 1 Introduction
• 2 Description of the Troubleshooting System Currently Used by the Power Suppliers
• 3 Mathematical Background of the Proposed Method
• 4 Fault Probability Data Bank
• 5 SGFI Algorithm
• 5.1 Localization of the Fault Zone (Procedure 1)

• 5.2 Fault Position Localization in the Fault Zone (Procedure 2)
• 5.3 Result of the Proposed Algorithm
• 5.4 Simulation Analysis of the SGFI Algorithm
• 6 Conclusions
• References
• Probability Distribution Functions for Short-Term Wind Power Forecasting
• 1 Introduction
• 2 Wind Probability Distribution Functions
• 2.1 Weibull Distribution
• 2.2 Lindley Distribution for Random Variables
• 2.3 Joint Probability Distribution Function for Wind Forecasting
• 3 Short-Term Wind Power Forecasting Using SVR and ANN
• 3.1 Wind Power Forecasting Using Support Vector Regressors
• 3.2 Wind Power Forecasting Using ANN.

This book presents the proceedings of the 8th International Workshop on Soft Computing Applications, SOFA 2018, held on 13-15 September 2018 in Arad, Romania. The workshop was organized by Aurel Vlaicu University of Arad, in conjunction with the Institute of Computer Science, Iasi Branch of the Romanian Academy, IEEE Romanian Section, Romanian Society of Control Engineering and Technical Informatics - Arad Section, General Association of Engineers in Romania - Arad Section and BTM Resources Arad. The papers included in these proceedings, published post-conference, cover the research including Knowledge-Based Technologies for Web Applications, Cloud Computing, Security Algorithms and Computer Networks, Business Process Management, Computational Intelligence in Education and Modelling and Applications in Textiles and many other areas related to the Soft Computing. The book is directed to professors, researchers, and graduate students in area of soft computing techniques and applications.
(source: Nielsen Book Data)

• Frontmatter
• CONTENTS
• PREFACE
• WHAT IS THIS BOOK ABOUT, ACCORDING TO AI?
• INTRODUCTION: TECHNOLOGY MOVES FASTER THAN JOURNALISTIC STANDARDS
• 1. THE PROBLEM: A JOURNALISTIC MODEL IN TRANSITION
• 2. ENABLERS: THE AI TECHNOLOGIES DRIVING JOURNALISTIC CHANGE
• 3. WORKFLOW: A SCALABLE PROCESS FOR NEWSROOM TRANSFORMATION
• CONCLUSION
• ACKNOWLEDGMENTS
• NOTES
• BIBLIOGRAPHY
• INDEX

Will the use of artificial intelligence (AI), algorithms, and smart machines be the end of journalism as we know it-or its savior? In Newsmakers, Francesco Marconi, who has led the development of the Associated Press and Wall Street Journal's use of AI in journalism, offers a new perspective on the potential of these technologies. He explains how reporters, editors, and newsrooms of all sizes can take advantage of the possibilities they provide to develop new ways of telling stories and connecting with readers. Marconi analyzes the challenges and opportunities of AI through case studies ranging from financial publications using algorithms to write earnings reports to investigative reporters analyzing large data sets to outlets determining the distribution of news on social media. Newsmakers contends that AI can augment-not automate-the industry, allowing journalists to break more news more quickly while simultaneously freeing up their time for deeper analysis. Marshaling insights drawn from firsthand experience, Marconi maps a media landscape transformed by artificial intelligence for the better. In addition to considering the benefits of these new technologies, Marconi stresses the continuing need for editorial and institutional oversight. Newsmakers outlines the important questions that journalists and media organizations should consider when integrating AI and algorithms into their workflow. For journalism students as well as seasoned media professionals, Marconi's insights provide much-needed clarity and a practical roadmap for how AI can best serve journalism.
(source: Nielsen Book Data)

• Mathematical Analysis of Nature-Inspired Algorithms.- A Review of No Free Lunch Theorems, and their Implications for Metaheuristic Optimisation.- Global Convergence Analysis of Cuckoo Search Using Markov Theory.- On Effeciently Solving the Vehicle Routing Problem with Time Windows Using the Bat Algorithm.- Variants of the Flower Pollination Algorithm: A Review.
• (source: Nielsen Book Data)

This book reviews the state-of-the-art developments in nature-inspired algorithms and their applications in various disciplines, ranging from feature selection and engineering design optimization to scheduling and vehicle routing. It introduces each algorithm and its implementation with case studies as well as extensive literature reviews, and also includes self-contained chapters featuring theoretical analyses, such as convergence analysis and no-free-lunch theorems so as to provide insights into the current nature-inspired optimization algorithms. Topics include ant colony optimization, the bat algorithm, B-spline curve fitting, cuckoo search, feature selection, economic load dispatch, the firefly algorithm, the flower pollination algorithm, knapsack problem, octonian and quaternion representations, particle swarm optimization, scheduling, wireless networks, vehicle routing with time windows, and maximally different alternatives. This timely book serves as a practical guide and reference resource for students, researchers and professionals.
(source: Nielsen Book Data)

• Analyzing the Effects of Alternative Decisions in a Multiagent System with Stigmergy-Based Interactions
• Modelling of Emotional Contagion in Soccer Fans
• An approach for Web Service selection and dynamic composition based on Linked Open Data
• Linguistic rules for ontology population from customer request
• Evolutionary Harmony Search Algorithm for sport scheduling problem
• Performance Analysis of Different Learning Algorithms of Feed Forward Neural Network Regarding Fetal Abnormality Detection
• DWIaaS: Data Warehouse Infrastructure as a Service for Big Data Analytics
• Hybrid Soft Computing for Atmospheric Pollution-Climate Change Data Mining
• Hiring Expert Consultants in E-Healthcare: An Analytics-Based Two Sided Matching Approach
• A fuzzy logic-based anticipation car-following model
• Fault-Tolerance in XJAF Agent Middleware.

These transactions publish research in computer-based methods of computational collective intelligence (CCI) and their applications in a wide range of fields such as the semantic web, social networks, and multi-agent systems. TCCI strives to cover new methodological, theoretical and practical aspects of CCI understood as the form of intelligence that emerges from the collaboration and competition of many individuals (artificial and/or natural). The application of multiple computational intelligence technologies, such as fuzzy systems, evolutionary computation, neural systems, consensus theory, etc., aims to support human and other collective intelligence and to create new forms of CCI in natural and/or artificial systems. This thirtieth issue is a regular issue with 12 selected papers.

• Preface.- Acknowledgements.- 1 From Natural to Synthetic Swarms.- 2 Glowworm Swarm Optimization: Algorithm Development.- 3 Theoretical Foundations.- 4 Multimodal Function Optimization.- 5 Experiments using Physical Simulations and Real Robots.- 6 Applications to Ubiquitous Computing Environments.- 7 Pursuit of Multiple Mobile Signal Sources.- 8 GSO Applications and Extensions.
• (source: Nielsen Book Data)

This book provides a comprehensive account of the glowworm swarm optimization (GSO) algorithm, including details of the underlying ideas, theoretical foundations, algorithm development, various applications, and MATLAB programs for the basic GSO algorithm. It also discusses several research problems at different levels of sophistication that can be attempted by interested researchers. The generality of the GSO algorithm is evident in its application to diverse problems ranging from optimization to robotics. Examples include computation of multiple optima, annual crop planning, cooperative exploration, distributed search, multiple source localization, contaminant boundary mapping, wireless sensor networks, clustering, knapsack, numerical integration, solving fixed point equations, solving systems of nonlinear equations, and engineering design optimization. The book is a valuable resource for researchers as well as graduate and undergraduate students in the area of swarm intelligence and computational intelligence and working on these topics.
(source: Nielsen Book Data)

• Part I Physical Security and Surveillance.- Part II: Cyber Security and Intrusion Detection Systems.- Part III: Biometric Security and Authentication Systems.- Part IV: Situational Awareness and Threat Assessment.- Part V: Strategic/Mission Planning and Resource Management.
• (source: Nielsen Book Data)

This volume is an initiative undertaken by the IEEE Computational Intelligence Society's Task Force on Security, Surveillance and Defense to consolidate and disseminate the role of CI techniques in the design, development and deployment of security and defense solutions. Applications range from the detection of buried explosive hazards in a battlefield to the control of unmanned underwater vehicles, the delivery of superior video analytics for protecting critical infrastructures or the development of stronger intrusion detection systems and the design of military surveillance networks. Defense scientists, industry experts, academicians and practitioners alike will all benefit from the wide spectrum of successful applications compiled in this volume. Senior undergraduate or graduate students may also discover uncharted territory for their own research endeavors.
(source: Nielsen Book Data)

• Toward a Restriction-Centered Theory of Truth and Meaning
• A Note on Logical Connectives with Weak Duality
• Residual Implications from Discrete Uninorms. A Characterization
• A Classiffication Theorem for Continuous Quasi-Uninorms
• Globally Monotone Extended Aggregation Functions
• A Survey of Contributions To Fuzzy Logic and its Applications to Artificial Intelligence at the IIIA
• Gradual Structures of Oppositions
• Generation of Interval-Valued Fuzzy Negations from Trillas' Theorem. The Case of Interval Type-2 Fuzzy Sets
• On Conjectures in T-Norm Based Fuzzy Logics
• Many-Valued Preorders I: The Basis of Many-Valued Mathematics
• Many-Valued Preorders II: The Symmetry Axiom and Probabilistic Geometry
• A Metasemantic Interpretation of Mamdani Systems
• Experimental Modeling for a Natural Landing of Fuzzy Sets in New Domains
• On Linguistic Variables and Sparse Representations
• Activating Generalized Fuzzy Implications from Galois Connections
• Randomness and Fuzziness: Combined Better than Unified
• Interpretability, a Silver Lining to a Fuzzy Cloud
• Prototype based Fuzzy Clustering Algorithms in High-Dimensional Feature Spaces
• Computing with Words for Decision Making versus Linguistic Decision Making: A Reflection on Both Scenarios
• Classification Validity Index
• On Fuzzy Modus Ponens to Assess Fuzzy Association Rules.-Applying Random Linear Oracles with Fuzzy Classifier Ensembles on WiFi Indoor Localization Problem
• Fuzzy Waves. Interference and Holography
• Fuzziness in CAR: Fuzzy Procedures for Robots and Cars
• Fuzzy Deformable Prototypes and Forest Fires Prediction and Prevention
• Hardware Implementation of Fuzzy Logic in Early Days of "Fuzzy" Era. Applying Random Linear Oracles with Fuzzy Classifier Ensembles on WiFi Indoor Localization Problem
• Fuzzy Waves. Interference and Holography
• Fuzziness in CAR: Fuzzy Procedures for Robots and Cars
• Fuzzy Deformable Prototypes and Forest Fires Prediction and Prevention
• Hardware Implementation of Fuzzy Logic in Early Days of "Fuzzy" Era. Generation of Interval-Valued Fuzzy Negations from Trillas' Theorem. The Case of Interval Type-2 Fuzzy Sets
• On Conjectures in T-Norm Based Fuzzy Logics
• Many-Valued Preorders I: The Basis of Many-Valued Mathematics
• Many-Valued Preorders II: The Symmetry Axiom and Probabilistic Geometry
• A Metasemantic Interpretation of Mamdani Systems
• Experimental Modeling for a Natural Landing of Fuzzy Sets in New Domains
• On Linguistic Variables and Sparse Representations
• Activating Generalized Fuzzy Implications from Galois Connections
• Randomness and Fuzziness: Combined Better than Unified
• Interpretability, a Silver Lining to a Fuzzy Cloud
• Prototype based Fuzzy Clustering Algorithms in High-Dimensional Feature Spaces
• Computing with Words for Decision Making versus Linguistic Decision Making: A Reflection on Both Scenarios
• Classification Validity Index
• On Fuzzy Modus Ponens to Assess Fuzzy Association Rules.-Applying Random Linear Oracles with Fuzzy Classifier Ensembles on WiFi Indoor Localization Problem
• Fuzzy Waves. Interference and Holography
• Fuzziness in CAR: Fuzzy Procedures for Robots and Cars
• Fuzzy Deformable Prototypes and Forest Fires Prediction and Prevention
• Hardware Implementation of Fuzzy Logic in Early Days of "Fuzzy" Era. On Fuzzy Modus Ponens to Assess Fuzzy Association Rules.-Applying Random Linear Oracles with Fuzzy Classifier Ensembles on WiFi Indoor Localization Problem
• Fuzzy Waves. Interference and Holography
• Fuzziness in CAR: Fuzzy Procedures for Robots and Cars
• Fuzzy Deformable Prototypes and Forest Fires Prediction and Prevention
• Hardware Implementation of Fuzzy Logic in Early Days of "Fuzzy" Era.

This book presents a comprehensive collection of the latest and most significant research advances and applications in the field of fuzzy logic. It covers fuzzy structures, rules, operations and mathematical formalisms, as well as important applications of fuzzy logic in a number of fields, like decision-making, environmental prediction and prevention, communication, controls and many others. Dedicated to Enric Trillas in recognition of his pioneering research in the field, the book also includes a foreword by Lotfi A. Zadeh and an outlook on the future of fuzzy logic.

• Performance Impact of New Interface for Non-Volatile Memory Storage
• Optimized Test Case Generation Based on Operational Profiles with Fault-Proneness Information
• Design Pattern for Self-adaptive RTE Systems monitoring
• Changing the Software Engineering Education: A Report from Current Situation in Mexico
• Analysis of Mouth Shape Deformation Rate for Generation of Japanese Utterance Images Automatically
• Predicting Fault-prone Modules by Word Occurrence in Identifiers
• The Influence of Alias and References Escape on Java Program Analysis
• Preliminary Evaluation of a Software Security Learning Environment
• Expression Strength for the Emotional Scene Detection from Lifelog Videos
• A Procedure for the Development of Mobile Applications Software
• The Relationships between Conversation Skill and Feeling of Load on Youth in Communicate with Elderly Persons using Video Image and Photographs
• Automatic Matching on Fracture Surface of Quarried Stone Using the Features of Plug-and-Feather Holes
• A Route Recommender System based on the User?s Visit Duration at Sightseeing Locations
• Predicting Access to Healthcare Using Data Mining Techniques
• Object Tracking Method Using PTAMM and Estimated Foreground Regions
• Application of Detecting Blinks for Communication Support Tool.

This edited book presents scientific results of the 12th International Conference on Software Engineering, Artificial Intelligence Research, Management and Applications (SERA 2014) held on August 31? September 4, 2014 in Kitakyushu, 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 and to share their experiences and exchange new ideas and information in a meaningful way. Research results about all aspects (theory, applications and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them. This publication captures 17 of the conference?s most promising papers.

• pt. 1. Ethical and philosophical issues of agent-based AmI systems
• pt. 2. Methods for development of agent-based AmI systems
• pt. 3. Towards more intelligent and adaptive agent-based AmI systems
• pt. 4. Applications of agent-based AmI systems.

The concept of an intelligent agent - a computational system capable of performing certain tasks autonomously - derived from the growing potential of digital computers in the mid 20th century and had been widely adopted by the early 1990s. Partly in parallel with this concept, the perspective of ambient intelligence (AmI) emerged in the late 1990s. Agent technology and AmI have many similarities, and the main purpose of this book is to provide an overview of the state-of-the-art of the scientific area that integrates these two. The book addresses a wide variety of topics related to agents and AmI, including theoretical, practical, design, implementation, ethical and philosophical issues. The 12 chapters are arranged in four sections. The first consists of three chapters discussing ethical and philosophical issues; the second part explores various approaches that can be used to develop agent-based AmI Systems; the third part contains three chapters that share the goal to endow AmI systems with useful properties like intelligence and adaptivity and the last section presents concrete applications of agent-based AmI systems. This book provides an insight into recent achievements and future challenges at the intersection of agent technology and ambient intelligence and will assist the development of more intelligent, flexible, effective and user-friendly systems as well as posing critical questions about the future of the role of agents within the AmI perspective.

• 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.

• Introduction
• Convex Probabilistic Wavelet like Approximation
• Bidimensional Constrained Wavelet like Approximation
• Multidimensional Probabilistic Scale Approximation
• Multidimensional probabilistic approximation in wavelet like structure.- About L-Positive Approximations: About Shape Preserving Weighted Uniform Approximation
• Jackson-Type Nonpositive Approximations for Definite Integrals.- Discrete Best L1 Approximation using the Gauges Way
• Quantitative Uniform Convergence of Smooth Picard Singular Integral Operators
• Global Smoothness and Simultaneous Approximation by Smooth Picard Singular Operators
• Convergence Results.- Approximation with Rates by Fractional Smooth Picard
• Singular Operators
• Multivariate Generalized Picard Singular Integral Operators
• Approximation by q-Gauss-Weierstrass Singular Integral Operators.- Quantitative Approximation by Univariate Shift-Invariant
• Integral Operators.

Currently the methods of Soft Computing are successfully used for risk analysis in: budgeting, e-commerce development, portfolio selection, Black-Scholes option pricing models, corporate acquisition systems, evaluating investments in advanced manufacturing technology, interactive fuzzy interval reasoning for smart web shopping, fuzzy scheduling and logistic. An essential feature of economic and financial problems it that there are always at least two criteria to be taken into account: profit maximization and risk minimization. Therefore, the economic and financial problems are multiple criteria.

• Contributing Authors.- Preface.- Foreword.- Genetic Programming: Theory and Practice.- A Population Based Study of Evolutionary Dynamics in Genetic Programming.- An Application of Information Theoretic Selection to Evolution of Models with Continuous-valued Inputs.- Pareto Cooperative-Competitive Genetic Programming: A Classification Benchmarking Study.- Genetic Programming with Historically Assessed Hardness.- Crossover and Sampling Biases on Nearly Uniform Landscapes.- Analysis of the Effects of Elitism on Bloat in Linear and Tree-based Genetic Programming.- Automated Extraction of Expert Domain Knowledge from Genetic Programming Synthesis Results.- Does Complexity Matter? Artificial Evolution, Computational Evolution and the Genetic Analysis of Epistasis in Common Human Diseases.- Exploiting Trustable Models via Pareto GP for Targeted Data Collection.- Evolving Effective Incremental SAT Solvers with GP.- Constrained Genetic Programming To Minimize Overfitting in Stock Selection.- Co-Evolving Trading Strategies to Analyze Bounded Rationality.- Profiling Symbolic Regression-Classification.- Accelerating Genetic Programming through Graphics Processing Units.- Genetic Programming for Incentive-Based Design within a Cultural Algorithms Framework.- Index.
• (source: Nielsen Book Data)

Genetic Programming Theory and Practice VI was developed from the sixth workshop at the University of Michigan's Center for the Study of Complex Systems to facilitate the exchange of ideas and information related to the rapidly advancing field of Genetic Programming (GP). Contributions from the foremost international researchers and practitioners in the GP arena examine the similarities and differences between theoretical and empirical results on real-world problems. The text explores the synergy between theory and practice, producing a comprehensive view of the state of the art in GP application. These contributions address several significant interdependent themes which emerged from this year's workshop, including: (1) Making efficient and effective use of test data. (2) Sustaining the long-term evolvability of our GP systems. (3) Exploiting discovered subsolutions for reuse. (4) Increasing the role of a Domain Expert.
(source: Nielsen Book Data)

• I Languages.- Executing Logical Agent Specifications.- IndiGolog: A High-Level Programming Language for Embedded Reasoning Agents.- Brahms An Agent-Oriented Language for Work Practice Simulation and Multi-Agent Systems Development.- ProgrammingRationalAgents in GOAL.- Merging Agents and Services - the JIAC Agent Platform.- Towards Pervasive Intelligence: Reflections on the Evolution of the Agent Factory Framework.- II Tools.- Debugging and Testing of Multi-Agent Systems using Design Artefacts.- Environment Programming in CArtAgO.- A Survey of Agent-oriented Development Tools.- III Applications.- A Multi-Agent Environment for Negotiation.
• (source: Nielsen Book Data)

Multi-Agent Systems are a promising technology to develop the next generation open distributed complex software systems. The main focus of the research community has been on the development of concepts (concerning both mental and social attitudes), architectures, techniques, and general approaches to the analysis and specification of multi-agent systems. This contribution has been fragmented, without any clear way of "putting it all together", rendering it inaccessible to students and young researchers, non-experts, and practitioners. Successful multi-agent systems development is guaranteed only if we can bridge the gap from analysis and design to effective implementation. Multi-Agent Programming: Languages, Tools and Applications presents a number of mature and influential multi-agent programming languages, platforms, development tools and methodologies, and realistic applications, summarizing the state of the art in an accessible manner for professionals and computer science students at all levels.
(source: Nielsen Book Data)

• 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)

• Cover
• Contents
• Preface
• 1 Introduction
• 1.1 Background
• 2 Mobile Agent System
• 2.1 Components of a Mobile Agent System
• 2.2 Characteristics and Advantages of a Mobile Agent System
• 3 Attacks and Countermeasures of Software System Security
• 3.1 General Security Objectives
• 3.2 Types of Attacks
• 3.2.1 Attacks against availability
• 3.2.2 Attacks against confidentiality
• 3.2.3 Attacks against integrity
• 3.2.4 Attacks against miscellaneous security objectives
• 3.3 Countermeasures of Attacks
• 3.3.1 Authentication
• 3.3.2 Access control
• 3.3.3 Audit and intrusion detection
• 3.3.4 Cryptography
• 3.3.5 Firewall
• 3.3.6 Anti-virus software
• 4 Security Issues in a Mobile Agent System
• 4.1 Security Issues in a Mobile Agent System
• 4.1.1 Possible attacks to a mobile agent system
• 4.1.2 Security requirements for a secure mobile agent system &

A mobile agent system could be attacked by malicious agents, platforms and third parties. Mobile agents simply offer greater opportunities for abuse and misuse, which broadens the scale of threats significantly. In addition, since mobile agents have some unique characteristics such as their mobility, security problems have become more complicated in these systems. These security problems have become a bottleneck in the development and maintenance of mobile agent systems, especially in security sensitive applications such as electronic commerce. This book introduces the concept and structure of mobile agent systems and discusses various attacks and countermeasures. The emphasis is on the formal modeling and analysis of secure mobile agent systems and their applications.

• Mobile Agent System
• Attack and Countermeasure
• Security Issues in a Mobile Agent System
• Formal Models
• A Formal Framework of a Generic Secure Mobile Agent System
• Translating the EEOS Model to Colored Petri Net Model
• Simulation and Analysis of the Extended Elementary Object System Model of a Secure Mobile Agent System
• A Case Study in Electronic Commerce
• A Case Study in E-Auction System.
• (source: Nielsen Book Data)

A mobile agent system could be attacked by malicious agents, platforms and third parties. Mobile agents simply offer greater opportunities for abuse and misuse, which broadens the scale of threats significantly. In addition, since mobile agents have some unique characteristics such as their mobility, security problems have become more complicated in these systems. These security problems have become a bottleneck in the development and maintenance of mobile agent systems, especially in security sensitive applications such as electronic commerce. This book introduces the concept and structure of mobile agent systems and discusses various attacks and countermeasures. The emphasis is on the formal modeling and analysis of secure mobile agent systems and their applications.
(source: Nielsen Book Data)

• Cover; Title page; Preface; Acknowledgments; Contents; I. Data Collection; Toward Active Mining from On-line Scientific Text Abstracts Using Pre-existing Sources; Data Mining on the WAVEs
• Word-of-mouth-Assisting Virtual Environments; Immune Network-based Clustering for WWW Information Gathering/Visualization; Interactive Web Page Retrieval with Relational Learning-based Filtering Rules; Monitoring Partial Update of Web Pages by Interactive Relational Learning; Context-based Classification of Technical Terms Using Support Vector Machines.

Focusing on data mining, this work is a joint effort from researchers in Japan, and includes a report on the forefront of data collection, user-centred mining and user interaction/reaction. It offers an overview of modern solutions with real-world applications, sharing hard-learned experiences.

• Introduction.- Logic and Inferencing.- First Order Predicate Logic.- Rule Based Reasoning.- Introduction to Prolog Language.- Knowledge Representation and Reasoning.- Networks Based Representation.- State Space Search.- Heuristic Search.- Constraint Satisfaction Problems.- Search and Game Playing.- Reasoning in Uncertain Environments.- Planning.- Natural Language Processing.- Machine Learning.- Machine Vision.- Bibliography.
• (source: Nielsen Book Data)

Fundamentals of Artificial Intelligence introduces the foundations of present day AI and provides coverage to recent developments in AI such as Constraint Satisfaction Problems, Adversarial Search and Game Theory, Statistical Learning Theory, Automated Planning, Intelligent Agents, Information Retrieval, Natural Language & Speech Processing, and Machine Vision. The book features a wealth of examples and illustrations, and practical approaches along with the theoretical concepts. It covers all major areas of AI in the domain of recent developments. The book is intended primarily for students who major in computer science at undergraduate and graduate level but will also be of interest as a foundation to researchers in the area of AI.
(source: Nielsen Book Data)

• A Combined Approach for Ontology Enrichment from Textual and Open Data.- C-SPARQL Extension for Sampling RDF Graphs Streams.- Efficiency Analysis of ASP Encodings for Sequential Pattern Mining Tasks.
• (source: Nielsen Book Data)

This book is a collection of representative and novel works in the field of data mining, knowledge discovery, clustering and classification. Discussing both theoretical and practical aspects of "Knowledge Discovery and Management" (KDM), it is intended for researchers interested in these fields, including PhD and MSc students, and researchers from public or private laboratories. The contributions included are extended and reworked versions of six of the best papers that were originally presented in French at the EGC'2016 conference held in Reims (France) in January 2016. This was the 16th edition of this successful conference, which takes place each year, and also featured workshops and other events with the aim of promoting exchanges between researchers and companies concerned with KDM and its applications in business, administration, industry and public organizations. For more details about the EGC society, please consult egc.asso.fr.
(source: Nielsen Book Data)

• Fuzzy Numbers.- Dual Numbers.- Fuzzy Dual Numbers.- Fuzzy Dual Linear Matrix Inequalities.- Fuzzy Estimation of Distribution Matrices.- Fuzzy Dual Entropy and Applications.- Fuzzy Dual Programming.
• (source: Nielsen Book Data)

This is the first book focusing exclusively on fuzzy dual numbers. In addition to offering a concise guide to their properties, operations and applications, it discusses some of their advantages with regard to classical fuzzy numbers, and describes the most important operations together with a set of interesting applications in e.g. optimization, decision-making and system design. The book provides students, researchers and professionals the necessary theoretical background to apply this particular subset of fuzzy numbers to decision-making problems involving uncertainty. Further, it shows how to solve selected engineering and management problems and includes detailed numerical examples.
(source: Nielsen Book Data)

• Introduction.- Pairwise Comparison Matrices.- Fuzzy Set Theory.- Fuzzy Pairwise Comparison Matrices.- Incomplete Large-dimensional Pairwise Comparison Matrices.- Discussion and Future Research. .
• (source: Nielsen Book Data)

This book offers the first comprehensive and critical literature review of fuzzy pairwise comparison methods derived from methods originally developed for crisp pairwise comparison matrices. It proposes new fuzzy extensions of these methods and provides a detailed study of the differences and analogies between all the reviewed methods, as well as a detailed description of their drawbacks, with the help of many numerical examples. In order to prevent the drawbacks related to the reviewed fuzzy pairwise comparison methods, the book introduces constrained fuzzy arithmetic in fuzzy extension of the pairwise comparison methods. It proposes new fuzzy pairwise comparison methods based on constrained fuzzy arithmetic and critically compares them with the reviewed methods. It describes the application of the newly developed methods to incomplete large-dimensional pairwise comparison matrices showcased in a real-life case study. Written for researchers, graduate and PhD students interested in multi-criteria decision making methods based on both crisp and fuzzy pairwise comparison matrices, this self-contained book offers an overview of cutting-edge research and all necessary information to understand the described tools and use them in real-world applications.
(source: Nielsen Book Data)

• Intro; Title Page; Contents; Introduction; Motivation; Problem Statement; Challenges and Overview of the State-of-the-Art; Challenges for Efficient SPARQL Query Processing; Challenges for Effective SPARQL Query Processing; Hypotheses and Research Questions; Contributions; Outline; Foundations of Linked Data Management; Linked Data; The Resource Description Framework (RDF); Querying RDF Data: The SPARQL Query Language; Querying RDF Data on the Web; URI Dereferencing and Link Traversal; SPARQL Endpoints; Linked Data Fragments; Foundations of Query Processing; Query Optimization

• Adaptive Query ProcessingAdaptive Query Processing over Linked Data; Introduction; Research Questions; Contributions; Structure of the Chapter; Motivating Example; Related Work; Adaptive Link Traversal Approaches; Adaptive Query Processing Against SPARQL Endpoints; Query Processing Approaches Against TPF Servers; The nLDE Approach; nLDE Query Optimizer; Estimation of Query Plan Cardinalities; Placing Physical Operators; Building Query Tree Plans; nLDE Optimizer: Algorithm Description; Complexity of the nLDE Query Optimizer; nLDE Adaptive Routing Query Engine; Adaptive Operators; Eddies

• Network of Linked Data Eddies (nLDE)Termination of nLDE; Correctness of nLDE; Routing Policies; Routing Policy from Eddies to Adaptive Operators; Routing Policy from Adaptive Operators to Eddies; Experimental Study; Experimental Settings; Efficiency of the nLDE Optimizer; Effectiveness of the nLDE Optimizer; Impact of the nLDE Routing-based Adaptivity on Execution Time in Perfect Networks; Effectiveness of the nLDE Routing-based Adaptivity Under the Presence of Network Delays; Summary and Future Work; Foundations of Crowdsourcing; Overview; Types of Crowdsourcing; Microtasks; Contests

• Crowdsourcing WorkflowsHybrid Crowdsourcing Workflows; Human-based Workflow: Find-Fix-Verify; Crowdsourcing Query Answer Completeness over Linked Data; Introduction; Research Questions; Contributions; Structure of the Chapter; Motivating Example; Related Work; Hybrid Query Processing for Relational Data; Crowd-based Linked Data Management Applications; Web Data Quality Assessment; The HARE Approach; Problem Definition; Proposed Solution; RDF Completeness Model; Representation of the Crowd Knowledge; Crowd Contradiction; Crowd Unknownness; HARE Microtask Manager; User Interface Generator

• Microtask ExecutorHARE Query Optimizer; Complexity of the HARE Query Optimizer; HARE Query Engine; A SPARQL Fuzzy Set Semantics; HARE BGP Executor; Complexity of HARE Query Evaluation; Experimental Study; Experimental Settings; HARE Crowdsourcing Capabilities; Size of Query Answer; Quality of Crowd Answers; Crowd Response Time; Summary and Future Work; Crowdsourcing Linked Data Quality Issues; Introduction; Research Questions; Contributions; Structure of the Chapter; Preliminaries: Linked Data Quality Issues; Related Work; Crowdsourcing in Linked Data Management; Web Data Quality Assessment

• Sequential Predictive Scheduling in Partitioned Data Domains.- Wildfire Optimizations in Modeling and Calibrations for Bulgarian Test Cases.- Process Control with the Variability Constraints.- Comparison of Different ACO Start Strategies Based on InterCriteria Analysis.- Evolutionary Approach for Tuning of Longwall Scraper Conveyor Model.- Solving Graph Partitioning Problems with Parallel Metaheuristics.- Comparison of Selected Fuzzy PSO Algorithms.
• (source: Nielsen Book Data)

This book presents new optimization approaches and methods and their application in real-world and industrial problems, and demonstrates how many of the problems arising in engineering, economics and other domains can be formulated as optimization problems. Constituting a comprehensive collection of extended contributions from the 9th International Workshop on Computational Optimization (WCO) held in Gdansk, Poland, September 11-14, 2016, the book discusses important applications such as job scheduling, wildfire modeling, parameter settings for controlling different processes, capital budgeting, data mining, finding the location of sensors in a given network, identifying the conformation of molecules, algorithm correctness, decision support system, and computer memory management. Further, it shows how to develop algorithms for these based on new intelligent methods like evolutionary computations, ant colony optimization and constraint programming. The book is a valuable resource for researchers and practitioners alike.
(source: Nielsen Book Data)

• A Review of Soft Computing Techniques in Maritime Logistics and its Related Fields.- Intelligent Data Analysis, Soft Computing and Imperfect Data.- Soft Computing Methods in Transport and Logistics.- Applications of Soft Computing in Intelligent Transportation Systems.- Fuzzy Cognitive Maps based Models for Pattern Classification: Advances and Challenges.- A proposal of on-line detection of new faults and automatic learning in fault diagnosis.- Fuzzy Portfolio Selection Models for dealing with Investor's Preferences.- Gradual numbers and fuzzy solutions to fuzzy optimization problems.- A modal account of preference in a fuzzy setting.- The single parameter family of Gini Bonferroni welfare functions and the binomial decomposition, transfer sensitivity and positional transfer sensitivity.
• (source: Nielsen Book Data)

This book offers a timely snapshot of current soft-computing research and solutions to decision-making and optimization problems, which are ubiquitous in the current social and technological context, addressing fields including logistics, transportation and data analysis. Written by leading international experts from the United States, Brazil and Cuba, as well as the United Kingdom, France, Finland and Spain, it discusses theoretical developments in and practical applications of soft computing in fields where these methods are crucial to obtaining better models, including: intelligent transportation systems, maritime logistics, portfolio selection, decision- making, fuzzy cognitive maps, and fault detection. The book is dedicated to Professor Jose L. Verdegay, a pioneer who has been actively pursuing research in fuzzy sets theory and soft computing since 1982, in honor of his 65th birthday.
(source: Nielsen Book Data)

• Sequence Classification based on Active Learning.- Using MongoDB Databases For Training and Combining Intrusion Detection Datasets.- Service Oriented Network Management with Knowledge-Based Network Management System in Fluctuating Environment.- A Topic Structuration Method on Time Series for a Meeting from Text Data.- Joint Embedding of Hierarchical Structure and Context for Entity Disambiguation.- Automatic Optimization of OpenCL-Based Stencil Codes for FPGAs.- Should Duration and Team Size be Used for Effort Estimation?.- A Lifelog Data Portfolio for Privacy Protection based on Dynamic Data Attributes in a Lifelog Service.- Estimation of Emotional Scene from Lifelog Videos in Consideration of Intensity of Various Facial Expressions.- Developing a Framework to Support Designing of Active Learning Class.- Where Do Drivers Look When Driving in a Foreign Country?.- On Password Strength: A Survey and Analysis.- Probabilistic Model-Based Multistep Crossover Considering Dependency between Nodes in Tree Optimization.- Digital Watermark Design for Two-dimensional Codes Displayed on Smart Phone Screen Using Multi-Objective Optimization and Optical Simulation.
• (source: Nielsen Book Data)

This book gathers 14 of the most promising papers presented at the 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2017), which was held on June 26-28, 2017 in Kanazawa, 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 various fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. The book presents research findings concerning all aspects (theory, applications and tools) of computer and information science, and discusses the practical challenges encountered along the way, as well as the solutions adopted to solve them.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Intelligence computer data analysis techniques
• 3. The fundamentals of Cognitive Informatics
• 4. Cognitive information systems
• 5. The fundamentals of management sciences
• 6. Intelligent cognitive information systems in management application
• 7. Summary.
• (source: Nielsen Book Data)

Cognitive Information Systems in Management Sciences summarizes the body of work in this area, taking an analytical approach to interpreting the data, while also providing an approach that can be used for practical implementation in the fields of computing, economics, and engineering. Using numerous illustrative examples, and following both theoretical and practical results, Dr. Lidia Ogiela discusses the concepts and principles of cognitive information systems, the relationship between intelligent computer data analysis, and how to utilize computational intelligent approaches to enhance information retrieval. Real world implantation use cases round out the book, with valuable scenarios covering management science, computer science, and engineering. Indexing: The books of this series are submitted to EI-Compendex and SCOPUS.
(source: Nielsen Book Data)

Annotation The Symposium series refers to all apects of theory, algorithm design, applications and emerging techniques related to computational intelligence.

• Part I: Online learning of a weighted selective naive Bayes classifier with non-convex optimization
• On making skyline queries resistant to outliers
• Adaptive Down-Sampling and Dimension Reduction in Time Elastic Kernel Machines for Efficient Recognition of Isolated Gestures
• Exact and Approximate Minimal Pattern Mining
• Part II: Comparison of proximity measures for a topological discrimination
• Comparison of linear modularization criteria using the relational formalism, an approach to easily identify resolution limit
• A novel approach to feature selection based on quality estimation metrics
• Ultrametricity of Dissimilarity Spaces and Its Significance for Data Mining
• Part III: SMERA: Semantic Mixed Approach for Web Query Expansion and Reformulation
• Multi-layer ontologies for integrated 3D shape segmentation and annotation
• Ontology Alignment Using Web Linked Ontologies as Background Knowledge
• LIAISON: reconciLIAtion of Individuals profiles across SOcial Networks
• Clustering of Links and Clustering of Nodes: Fusion of Knowledge in Social Networks.

This book presents a collection of representative and novel work in the field of data mining, knowledge discovery, clustering and classification, based on expanded and reworked versions of a selection of the best papers originally presented in French at the EGC 2014 and EGC 2015 conferences held in Rennes (France) in January 2014 and Luxembourg in January 2015. The book is in three parts: The first four chapters discuss optimization considerations in data mining. The second part explores specific quality measures, dissimilarities and ultrametrics. The final chapters focus on semantics, ontologies and social networks. Written for PhD and MSc students, as well as researchers working in the field, it addresses both theoretical and practical aspects of knowledge discovery and management.

• Introduction
• Basic Notions
• Classical Sentential Calculus and Lukasiewicz Sentential Calculus
• MV -Algebras: Generalities
• Local MV -algebras
• Perfect MV -algebras
• The Variety Generated by Perfect MV -algebras
• Representations of Perfect MV -algebras
• The Logic of Perfect Algebras
• The Logic of Quasi True
• Perfect Pavelka Logic.

This book presents the first algebraic treatment of quasi-truth fuzzy logic and covers the algebraic foundations of many-valued logic. It offers a comprehensive account of basic techniques and reports on important results showing the pivotal role played by perfect many-valued algebras (MV-algebras). It is well known that the first-order predicate Łukasiewicz logic is not complete with respect to the canonical set of truth values. However, it is complete with respect to all linearly ordered MV -algebras. As there are no simple linearly ordered MV-algebras in this case, infinitesimal elements of an MV-algebra are allowed to be truth values. The book presents perfect algebras as an interesting subclass of local MV-algebras and provides readers with the necessary knowledge and tools for formalizing the fuzzy concept of quasi true and quasi false. All basic concepts are introduced in detail to promote a better understanding of the more complex ones. It is an advanced and inspiring reference-guide for graduate students and researchers in the field of non-classical many-valued logics.

• 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.

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

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

• 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

• Part I. Density Ratio Approach to Machine Learning: 1. Introduction
• Part II. Methods of Density Ratio Estimation: 2. Density estimation
• 3. Moment matching
• 4. Probabilistic classification
• 5. Density fitting
• 6. Density-ratio fitting
• 7. Unified framework
• 8. Direct density-ratio estimation with dimensionality reduction
• Part III. Applications of Density Ratios in Machine Learning: 9. Importance sampling
• 10. Distribution comparison
• 11. Mutual information estimation
• 12. Conditional probability estimation
• Part IV. Theoretical Analysis of Density Ratio Estimation: 13. Parametric convergence analysis
• 14. Non-parametric convergence analysis
• 15. Parametric two-sample test
• 16. Non-parametric numerical stability analysis
• Part V. Conclusions: 17. Conclusions and future directions.
• (source: Nielsen Book Data)

Machine learning is an interdisciplinary field of science and engineering that studies mathematical theories and practical applications of systems that learn. This book introduces theories, methods and applications of density ratio estimation, which is a newly emerging paradigm in the machine learning community. Various machine learning problems such as non-stationarity adaptation, outlier detection, dimensionality reduction, independent component analysis, clustering, classification and conditional density estimation can be systematically solved via the estimation of probability density ratios. The authors offer a comprehensive introduction of various density ratio estimators including methods via density estimation, moment matching, probabilistic classification, density fitting and density ratio fitting, as well as describing how these can be applied to machine learning. The book provides mathematical theories for density ratio estimation including parametric and non-parametric convergence analysis and numerical stability analysis to complete the first and definitive treatment of the entire framework of density ratio estimation in machine learning.
(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)

• 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.

• 1. Introduction
• 2. Clusters and robots
• 3. Cluster programming
• 4. Robot motion
• 5. Sensors
• 6. Mapping and localization
• 7. Vision and tracking
• 8. Learning landmarks
• 9. Robot architectures
• Appendix I: Summary of OpenMPI man page for mpirun
• Appendix II: MPI datatypes
• Appendix III: MPI reduction operations
• Appendix IV: MPI application programmer interface.

In this book, we look at how cluster technology can be leveraged to build better robots. Algorithms and approaches in key areas of robotics and computer vision, such as map building, target tracking, action selection and landmark learning, are reviewed and cluster implementations for these are presented.The objective of the book is to give professionals working in the beowulf cluster or robotics and computer vision fields a concrete view of the strong synergy between the areas as well as to spur further fruitful exploitation of this connection. The book is written at a level appropriate for an advanced undergraduate or graduate student. The key concepts in robotics, computer vision and cluster computing are introduced before being used to make the text useful to a wide audience in these fields.
(source: Nielsen Book Data)

• 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

• Preface
• Agent-Based Genetic Algorithm for Global Numerical Optimization & Feature Selection
• Multi-Agent Enterprise Sustainability Performance Measurement System
• A Modular Artificial Neural Network Based Decision Making in a Multi-Agent Robot Soccer System
• Security & Privacy in Track & Trace Infrastructures
• A Challenge to Develop Large-Scale Agent Simulation Software
• A Framework of an Agent-Based Model Using Social & Physical Interaction for Vulnerability Analysis on Flood Events
• An Investigation into the Issues of Multi-Agent Data Mining
• Agent-Based Discovery, Composition & Orchestration of Grid Web Services
• An Efficient Mobile-Agent-Based Platform for Dynamic Service Provisioning in 3G/UMTs
• Index.
• (source: Nielsen Book Data)

Multi-agent systems (MAS) often deal with complex applications that require distributed problem solving. In many applications, the individual and collective behaviour of the agents depends on the observed data from distributed sources. This book discusses a number of research issues concerned with the use of Multi-Agent Systems for Data Mining (MADM), also known as agent-driven data mining. In addition, optimisation algorithms are very important in modern research and industrial areas. This book examines one multi-population co-genetic algorithm (MPAGA) with double chain-like agent structure to realise parallel optimisation, combining chain-like agent structure and multi-population parallel searching. Furthermore, this book proposes an efficient modular artificial neural network (ANN) architecture for the intelligent decision making of a robot in a robot soccer systems with different team configurations. Other chapters review the use of radio frequency identification (RFID) technology with supply chain agents and then analyse the security requirements, describe how to design and implement a large-scale multi-agent simulation software, and provide a framework of evacuation simulation for urban hazards such as flooding with effective agent's interaction tools with other agents and the physical environment.
(source: Nielsen Book Data)

• 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.

This book presents the proceedings of the 2018 International Conference on Security with Intelligent Computing and Big-data Services (SICBS 2018). With the proliferation of security with intelligent computing and big-data services, the issues of information security, big data, intelligent computing, blockchain technology, and network security have attracted a growing number of researchers. Discussing topics in areas including blockchain technology and applications; multimedia security; information processing; network, cloud and IoT security; cryptography and cryptosystems; as well as learning and intelligent computing and information hiding, the book provides a platform for researchers, engineers, academics and industrial professionals from around the globe to present their work in security-related areas. It not only introduces novel and interesting ideas, but also stimulates discussions and inspires new ideas.

• Part I Fuzzy, Neuro-Fuzzy and Rough Sets Applications.- Part II Neural Network Applications.- Part III Applications of Evolutionary Computations.- Part IV Other Soft Computing Applications.- Part V Design of Fuzzy, Neuro-Fuzzy and Rough Sets Techniques.- Part VI Design of Evolutionary Computation Techniques.- Part VII Design for other Soft Computing Techniques.- Part VIII Computer Graphics, Imaging, Vision and Signal Processing.
• (source: Nielsen Book Data)

The World Soft Computing (WSC) conference is an annual international online conference on applied and theoretical soft computing technology. The topics covered by the conference range from applied to theoretical aspects of fuzzy, neuro-fuzzy and rough sets over to neural networks to single and multi-objective optimisation. Contributions about particle swarm optimisation, gene expression programming, clustering, classi cation, support vector machines, quantum evolution and agent systems have also been received. One whole session was devoted to soft computing techniques in computer graphics, imaging, vision and signal processing.

• Intelligent Environments: Methods, Algorithms and Applications.- A Pervasive Sensor System for Evidence-Based Nursing Care Support.- Anomalous Behavior Detection: Supporting Independent Living.- Sequential PatternMining for Cooking-Support Robot.- Robotic, Sensory and Problem-Solving Ingredients for the Future Home.- Ubiquitous Stereo Vision for Human Sensing.- Augmenting Professional Training, an Ambient Intelligence Approach.- Stereo Omnidirectional System (SOS) and Its Applications.- Video Analysis for Ambient Intelligence in Urban Environments.- From Monomodal to Multimodal: Affect Recognition Using Visual Modalities.- Importance of Vision in Human-Robot Communication Understanding Speech Using Robot Vision and Demonstrating Proper Actions to Human Vision.
• (source: Nielsen Book Data)

Relatively new research ?elds such as ambient intelligence, intelligent envir- ments, ubiquitous computing, and wearable devices have emerged in recent years. These ?elds are related by a common theme: making use of novel technologies to enhance user experience by providing user-centric intelligent environments, - moving computers from the desktop and making computing available anywhere and anytime. It must be said that the concept of intelligent environments is not new and beganwithhomeautomation. Thechoiceofnameforthe?eldvariessomewhatfrom continent to continent in the English-speaking world. In general intelligent space is synonymous to intelligent environments or smart spaces of which smart homes is a sub?eld. In this collection, the terms intelligent environments and ambient int- ligence are used interchangeably throughout. Such environments are made possible by permeating living spaces with intelligent technology that enhances quality of life. In particular, advances in technologies such as miniaturized sensors, advances in communication and networking technology including high-bandwidth wireless devices and the reduction in power consumption have made possible the concept of intelligent environments. Environments such as a home, an of?ce, a shopping mall, and a travel port utilize data provided by users to adapt the environment to meet the user's needs and improve human-machine interactions. The user information is gathered either via wearable devices or by pervasive sensors or a combination of both. Intelligent environments brings together a number of research ?elds from computer science, such as arti?cial intelligence, computer vision, machine learning, and robotics as well as engineering and architecture.
(source: Nielsen Book Data)

• A Metamodel-Assisted Steady-State Evolution Strategy for Simulation-Based Optimization.- Automatically Defined Groups for Knowledge Acquisition from Computer Logs and Its Extension for Adaptive Agent Size.- Robust Hybrid Sliding Mode Control for Uncertain Nonlinear Systems Using Output Recurrent CMAC.- A Dynamic GA-Based Rhythm Generator.- Evolutionary Particle Swarm Optimization: A Metaoptimization Method with GA for Estimating Optimal PSO Models.- Human-Robot Interaction as a Cooperative Game.- Swarm and Entropic Modeling for Landmine Detection Robots.- Iris Recognition Based on 2D Wavelet and AdaBoost Neural Network.- An Improved Multiclassifier for Soft Fault Diagnosis of Analog Circuits.- The Effect of Background Knowledge in Graph-Based Learning in the Chemoinformatics Domain.- Clustering Dependencies with Support Vectors.- A Comparative Study of Gender Assignment in a Standard Genetic Algorithm.- PSO Algorithm for Primer Design.- Genetic Algorithms and Heuristic Rules for Solving the Nesting Problem in the Package Industry.- MCSA-CNN Algorithm for Image Noise Cancellation.- An Integrated Approach Providing Exact SNP IDs from Sequences.- Pseudo-Reverse Approach in Genetic Evolution.- Microarray Data Feature Selection Using Hybrid GA-IBPSO.- Discrete-Time Model Representations for Biochemical Pathways.- Performance Evaluation of Decision Tree for Intrusion Detection Using Reduced Feature Spaces.- Novel and Efficient Hybrid Strategies for Constraining the Search Space in Frequent Itemset Mining.- Detecting Similar Negotiation Strategies.- Neural Networks Applied to Medical Data for Prediction of Patient Outcome.- Prediction Method for Real Thai Stock Index Based on Neurofuzzy Approach.- Innovative Technology Management System with Bibliometrics in the Context of Technology Intelligence.- Cobweb/IDX: Mapping Cobweb to SQL.- Interoperability of Performance and Functional Analysis for Electronic System Designs in Behavioural Hybrid Process Calculus (BHPC).- Partitioning Strategy for Embedded Multiprocessor FPGA Systems.- Interpretation of Sound Tomography Image for the Recognition of Ganoderma Infection Level in Oil Palm.- A Secure Multiagent Intelligent Conceptual Framework for Modeling Enterprise Resource Planning.- On Generating Algebraic Equations for A5-Type Key Stream Generator.- A Simulation-Based Study on Memory Design Issues for Embedded Systems.- SimDiv: A New Solution for Protein Comparison.- Using Filtering Algorithm for Partial Similarity Search on 3D Shape Retrieval System.- Topic-Specific Language Model Based on Graph Spectral Approach for Speech Recognition.- Automatic Construction of FSA Language Model for Speech Recognition by FSA DP-Matching.- Density: A Context Parameter of Ad Hoc Networks.- Integrating Design by Contract Focusing Maximum Benefit.- Performance Engineering for Enterprise Applications.- A Framework for UML-Based Software Component Testing.- Extending the Service Domain of an Interactive Bounded Queue.- A Hybrid Evolutionary Approach to Cluster Detection.- Transforming the Natural Language Text for Improving Compression Performance.- Compression Using Encryption.
• (source: Nielsen Book Data)

This volume contains contributions from participants in the 2007 International Multiconference of Engineers and Computer Scientists. It covers a variety of subjects in the frontiers of intelligent systems and computer engineering and their industrial applications. The book offers up-to-date information on advances in intelligent systems and computer engineering and also serves as an excellent reference work for researchers and graduate students working in the field.
(source: Nielsen Book Data)

• Title page; Preface; Contents; Part I: General Purpose Applications of AI; Supervised Machine Learning: A Review of Classification Techniques; Dimension Reduction and Data Visualization Using Neural Networks; Recommender System Technologies Based on Argumentation; Knowledge Modelling Using UML Profile for Knowledge-Based Systems Development; A Semantic-Based Navigation Approach for Information Retrieval in the Semantic Web; Ontology-Based Management of Pervasive Systems; A DIYD (Do It Yourself Design) e-Commerce System for Vehicle Design Based on Ontologies and 3D Visualization.

Provides insights on how computer engineers can implement artificial intelligence (AI) in real world applications. This book presents practical applications of AI.

• 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.

• Part I Motivation for and Introduction to Mobile Agents
• 1 Designing Innovative Distributed Systems
• 2 From Client-Server to Mobile Agents
• Part II Mobile Agents Concepts, Functions, and Possible Problems
• 3 Mobile Agent Migration
• 4 Mobile Agent Communication
• 5 Mobile Agent Security
• Part III The Kalong Mobility Model Specification and Implementation
• Chapter 6 Specifications of the Kalong Mobility Model
• Chapter 7 Using Kalong
• Chapter 8 Evaluation
• Part IV The Tracy Mobile Agent Toolkit
• Chapter 9 Running a Tracy Agency
• Chapter 10 Programming Agents with Tracy
• Bibliography
• Index.
• (source: Nielsen Book Data)

Mobile agents are software nomads that act as your personal representative, working autonomously through networks. They are able to visit network nodes directly using available computing power and are not limited by platform. This emerging field is now poised to become a cornerstone for new Web-based ubiquitous computing environments. Mobile Agents provides a practical introduction to mobile agent technology and surveys the state of the art in mobile agent research. Students and researchers can use the book as an introduction to the concepts and possibilities of this field and as an overview of ongoing research. Developers can use it to identify the capabilities of the technology to decide if mobile agents are the right solution for them. Practioners can also gain hands-on experience in programming mobile agents through exploration of the source code for a complete mobile agent environment available through the companion website.
(source: Nielsen Book Data)

• Part I Motivation for and Introduction to Mobile Agents
• 1 Designing Innovative Distributed Systems
• 2 From Client-Server to Mobile Agents
• Part II Mobile Agents Concepts, Functions, and Possible Problems
• 3 Mobile Agent Migration
• 4 Mobile Agent Communication
• 5 Mobile Agent Security
• Part III The Kalong Mobility Model Specification and Implementation
• Chapter 6 Specifications of the Kalong Mobility Model
• Chapter 7 Using Kalong
• Chapter 8 Evaluation
• Part IV The Tracy Mobile Agent Toolkit
• Chapter 9 Running a Tracy Agency
• Chapter 10 Programming Agents with Tracy
• Bibliography
• Index.
• (source: Nielsen Book Data)

Mobile agents are software nomads that act as your personal representative, working autonomously through networks. They are able to visit network nodes directly using available computing power and are not limited by platform. This emerging field is now poised to become a cornerstone for new Web-based ubiquitous computing environments. Mobile Agents provides a practical introduction to mobile agent technology and surveys the state of the art in mobile agent research. Students and researchers can use the book as an introduction to the concepts and possibilities of this field and as an overview of ongoing research. Developers can use it to identify the capabilities of the technology to decide if mobile agents are the right solution for them. Practioners can also gain hands-on experience in programming mobile agents through exploration of the source code for a complete mobile agent environment available through the companion website.
(source: Nielsen Book Data)

• Contributors
• Acknowledgments
• 1. An Introduction to the Third Volume / Lee Spector, William B. Langdon, Una-May O'Reilly, Peter J. Angelino
• I. Applications
• 2. An Automatic Software Re-Engineering Tool Based on Genetic Programming / Conor Ryan and Laur Ivan
• 3. CAD Surface Reconstruction from Digitized 3D Point Data with a Genetic Programming/Evolution Strategy Hybrid / Robert E. Keller, Wolfgang Banzhaf, Jorn Mehnen and Klaus Weinert
• 4. A Genetic Programming Approach for Robust Language Interpretation / Carolyn Penstein Rose
• 5. Time Series Modeling Using Genetic Programming: An Application to Rainfall-Runoff Models / Peter A. Whigham and Peter F. Crapper
• 6. Automatic Synthesis, Placement, and Routing of Electrical Circuits by Means of Genetic Programming / John R. Koza and Forest H. Bennett III
• 7. Quantum Computing Applications of Genetic Programming / Lee Spector, Howard Barnum, Herbert J. Bernstein and Nikhil Swamy
• II. Theory
• II. Theory
• 8. The Evolution of Size and Shape / William B. Langdon, Terry Soule, Riccardo Poli and James A. Foster
• 9. Fitness Distributions: Tools for Designing Efficient Evolutionary Computations / Christian Igel and Kumar Chellapilla
• 10. Analysis of Single-Node (Building) Blocks in Genetic Programming / Jason M. Daida, Robert R. Bertram, John A. Polito 2 and Stephen A. Stanhope
• 11. Rooted-Tree Schemata in Genetic Programming / Justinian P. Rosca and Dana H. Ballard
• III. Extensions
• III. Extensions
• 12. Efficient Evolution of Machine Code for CISC Architectures Using Instruction Blocks and Homologous Crossover / Peter Nordin, Wolfgang Banzhaf and Frank D. Francone
• 13. Sub-machine-code Genetic Programming / Riccardo Poli and William B. Langdon
• 14. The Internal Reinforcement of Evolving Algorithms / Astro Teller
• 15. Inductive Genetic Programming with Immune Network Dynamics / Nikolay I. Nikolaev, Hitoshi Iba and Vanio Slavov
• 16. A Self-Tuning Mechanism for Depth-Dependent Crossover / Takuyo Ito, Hitoshi Iba and Satoshi Sato
• 17. Genetic Recursive Regression for Modeling and Forecasting Real-World Chaotic Time Series / Geum Yong Lee
• 18. Co-evolutionary Fitness Switching: Learning Complex Collective Behaviors Using Genetic Programming / Byoung-Tak Zhang and Dong-Yeon Cho
• 19. Evolving Multiple Agents by Genetic Programming / Hitoshi Iba
• Index.

Genetic programming is a form of evolutionary computation that evolves programs and program-like executable structures for developing reliable time-and cost-effective applications. It does this by breeding programs over many generations, using the principles of natural selection, sexual recombination, and mutuation. This third volume of Advances in Genetic Programming highlights many of the recent technical advances in this increasingly popular field. Genetic programming is a form of evolutionary computation that evolves programs and program-like executable structures for developing reliable time-and cost-effective applications. It does this by breeding programs over many generations, using the principles of natural selection, sexual recombination, and mutuation. This third volume of Advances in Genetic Programming highlights many of the recent technical advances in this increasingly popular field.
(source: Nielsen Book Data)
Genetic programming is a form of evolutionary computation that evolves programs and program-like executable structures for developing reliable time -- and cost-effective applications. It does this by breeding programs over many generations, using the principles of natural selection, sexual recombination, and mutuation. This third volume of "Advances in Genetic Programming" highlights many of the recent technical advances in this increasingly popular field.
(source: Nielsen Book Data)

• Neural networks in systems identification and control - supervised learning in multilayer perceptions - the back-propagation algorithm
• identification of two-dimensional state space discrete systems using neural networks
• neural networks for control
• neuro-based adaptive regulator
• local model networks and self-tuning predictive control
• fuzzy and neuro-fuzzy systems in modelling, control and robot path planning - an on-line self constructing fuzzy modelling architecture based on neural and fuzzy concepts and techniques
• neuro-fuzzy model-based control
• fuzzy and neurofuzzy approaches to mobile robot path and motion planning under uncertainty
• genetic-evolutionary algorithms - a tutorial overview of genetic algorithms and their applications
• results from a variety of genetic algorithm applications showing the robustness of the approach
• evolutionary algorithms in computer-aided design of integrated circuits
• soft computing applications - soft data fusion
• application of neural networks to computer gaming
• coherent neural networks and their applications to control and signal processing
• neural, fuzzy and evolutionary reinforcement learning systems - an application case study
• neural networks in industrial and environmental applications.
• (source: Nielsen Book Data)

Soft computing is a branch of computing which, unlike hard computing, can deal with uncertain, imprecise and inexact data. The three constituents of soft computing are fuzzy-logic-based computing, neurocomputing, and genetic algorithms. Fuzzy logic contributes the capability of approximate reasoning, neurocomputing offers function approximation and learning capabilities, and genetic algorithms provide a methodology for systematic random search and optimization. These three capabilities are combined in a complementary and synergetic fashion.This book presents a cohesive set of contributions dealing with important issues and applications of soft computing in systems and control technology. The contributions include state-of-the-art material, mathematical developments, fresh results, and how-to-do issues. Among the problems studied via neural, fuzzy, neurofuzzy and genetic methodologies are: data fusion, reinforcement learning, approximation properties, multichannel imaging, signal processing, system optimization, gaming, and several forms of control.The book can serve as a reference for researchers and practitioners in the field. Readers can find in it a large amount of useful and timely information, and thus save considerable effort in searching for other scattered literature.
(source: Nielsen Book Data)

• 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)

• Part 1: Preparing for the Future of Work
• Chapter 1: Will Robots Replace You?
• Chapter 2: Technology Definitions
• Part 2: Robots are Working
• Chapter 3: Robotic Process Automation
• Chapter 4: Robots in Teams
• Chapter 5: Robots Without Arms
• Part 3: Making Sense for Robots and Society
• Chapter 6: Robots in a World of Data
• Chapter 7: Robots in Society
• Chapter 8: Work in the Future.

Understand the current and future research into technologies that underpin the increasing capabilities of automation technologies and their impact on the working world of the future. Rapid advances in automation and robotics technologies are often reported in the trade and general media, often relying on scary headlines such as "Jobs Lost to Robots." It is certainly true that work will change with the advent of smarter and faster automated workers; however, the scope and scale of the changes is still unknown. Automation may seem to be here already, but we are only at the early stages. Automation and Collaborative Robotics explores the output of current research projects that are improving the building blocks of an automated world. Research into collaborative robotics (cobotics) is merging digital, audio, and visual data to generate a commonly held view between cobots and their human collaborators. Low-power machine learning at the edge of the network can deliver decision making on cobots or to their manipulations. Topics covered in this book include: Robotic process automation, chatbots, and their impact in the near future The hype of automation and headlines leading to concerns over the future of work Component technologies that are still in the research labs Foundational technologies and collaboration that will enable many tasks to be automated with human workers being re-skilled and displaced rather than replaced What You Will Learn Be aware of the technologies currently being researched to improve or deliver automation Understand the impact of robotics, other automation technologies, and the impact of AI on automation Get an idea of how far we are from implementation of an automated future Know what work will look like in the future with the deployment of these technologies Who This Book Is For Technical and business managers interested in the future of automation and robotics, and the impact it will have on their organizations, customers, and the business world in general.
(source: Nielsen Book Data)

• I ntroduction.- Robot State Estimation.- Visual Servo.- Task Control.- Closing remarks.
• (source: Nielsen Book Data)

This monograph covers theoretical and practical aspects of the problem of autonomous guiding of unmanned aerial manipulators using visual information. For the estimation of the vehicle state (position, orientation, velocity, and acceleration), the authors propose a method that relies exclusively on the use of low-cost and highrate sensors together with low-complexity algorithms. This is particularly interesting for applications in which on board computation with low computation power is needed. Another relevant topic covered in this monograph is visual servoing. The authors present an uncalibrated visual servo scheme, capable of estimating at run time, the camera focal length from the observation of a tracked target. The monograph also covers several control techniques, which achieve a number of tasks, such as robot and arm positioning, improve stability and enhance robot arm motions. All methods discussed in this monograph are demonstrated in simulation and through real robot experimentation. The text is appropriate for readers interested in state estimation and control of aerial manipulators, and is a reference book for people who work in mobile robotics research in general.
(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)

• Learning for vision.- Computational photography.- Human analysis.- Human sensing.- Stereo and reconstruction.- Optimization.- Matching and recognition.- Video attention.- Poster sessions.
• (source: Nielsen Book Data)

The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.
(source: Nielsen Book Data)

• Learning for vision.- Computational photography.- Human analysis.- Human sensing.- Stereo and reconstruction.- Optimization.- Matching and recognition.- Video attention.- Poster sessions.
• (source: Nielsen Book Data)

The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.
(source: Nielsen Book Data)

• Learning for vision.- Computational photography.- Human analysis.- Human sensing.- Stereo and reconstruction.- Optimization.- Matching and recognition.- Video attention.- Poster sessions.
• (source: Nielsen Book Data)

The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.
(source: Nielsen Book Data)

• Part I: Distributed Algorithms and Optimization.- A Performance Analysis of Self-* Evolutionary Algorithms on Networks with Correlated Failures.- Spatially Structured Evolutionary Algorithms: Graph Degree, Population Size and Convergence Speed.- Heuristic of Anticipation for Fair Scheduling and Resource Allocation in Grid VOs.- On the Applications of Dijkstra's Shortest Path Algorithm in Software Defined Networks.- Part II: Reasoning and Decision Making in Distributed Environments.- Towards a Paraconsistent Approach to Actions in Distributed Information-Rich Environments.- A Modified Vickrey Auction with Regret Minimization for Uniform Alliance Decisions.- Lightweight Cooperative Self-Localization as Support to Traffic Regulation for Autonomous Car Driving.- A New Approach for Vertical Handover between LTE and WLAN Based on Fuzzy Logic and Graph Theory.- Part III: Multi-agent Systems.- Programming the Interaction Space Effectively with ReSpecTX.- Multi-Agent System to Design Next Generation of Airborne Platform.- A Drone-based Building Inspection System using Software-Agents.- Part IV: Data Analysis, Mining and Integration.- Connecting Social Media Data with Observed Hybrid Data for Environment Monitoring.- EUStress: a Human Behaviour Analysis System for Monitoring and Assessing Stress during Exams.- Post Sharing-based Credibility Network for Social Network.- Ontological Hybrid Storage for Security Data.- Part V: Machine Learning.- Wind Power Production Forecasting using Ant Colony Optimization and Extreme Learning Machines.- Convolutional Neural Networks for Four-Class Motor Imagery Data Classification.- Binary Classification of Images for Applications in Intelligent 3D Scanning.- Part VI: Internet of Things and Cloud Computing.- Context Aware Resource and Service Provisioning Management in Fog Computing Systems.- An Argumentative Approach to Smart Home Office Ambient Lighting.- A Recommender System Based on Hierarchical Clustering for Cloud e-Learning.- A Taxonomy of Anomalies in Distributed Cloud Systems: The CRI-Model.- Modeling and Analysis of IoT Energy Resource Exhaustion Attacks.- Part VII: Service-based Distributed Systems.- Service Discovery in Megascale Distributed Systems.- Context-Aware Access Control Model for Services Provided from Cloud Computing.- Reference Architecture for Self-Adaptive Microservice Systems.- Part VIII: WASA 2017 (7th Workshop on Applications of Software Agents).- Agent-Based Computing in the Internet of Things: a Survey.- Teaching, Learning and Assessment of Agents and Robotics in a Computer Science Curriculum.
• (source: Nielsen Book Data)

This book presents a collection of contributions addressing recent advances and research in synergistic combinations of topics in the joint fields of intelligent computing and distributed computing. It focuses on the following specific topics: distributed data mining and machine learning, reasoning and decision-making in distributed environments, distributed evolutionary algorithms, trust and reputation models for distributed systems, scheduling and resource allocation in distributed systems, intelligent multi-agent systems, advanced agent-based and service-based architectures, and Smart Cloud and Internet of Things (IoT) environments. The book represents the combined peer-reviewed proceedings of the 11th International Symposium on Intelligent Distributed Computing (IDC 2017) and the 7th International Workshop on Applications of Software Agents (WASA 2017), both of which were held in Belgrade, Serbia from October 11 to 13, 2017.
(source: Nielsen Book Data)

• Production and Economic Relations on the Internet: Another Level of Development of Economic Science.- Internet Economy: Existence from the Point of View of Micro-Economic Aspect.- Perspectives of Internet Economy Creation.- Modern Foundations of Internet Economy.
• (source: Nielsen Book Data)

This unique book adopts a problem approach to study the Internet economy, which consists in assessing its contradiction to classic economy, analyzing it, and describing how the Internet economy is developing in modern Russia. The authors study its sense and basic principles, identify the factors that influence its functioning and development, analyze the crisis of the Internet economy, and conduct a comparative analysis of the Internet economy and classic economy. The book is primarily intended for postgraduates, educators and researchers who study the foundations of the modern macro-economy. Based on the conclusions and results presented here, they will be able to create their own scientific studies. Further, problem analysis of the Internet economy and classic economy makes it possible to identify the peculiarities and prospects of development, and to form recommendations for the highly effective management of modern economic systems.
(source: Nielsen Book Data)

• Introduction.- Interval arithmetic.- Alternative arithmetic.- Interval and parametric interval matrices.- Linear systems.- Methods for solving parametric interval linear systems.- Methods for computing parametric solution.- Parametric interval linear programming problem.
• (source: Nielsen Book Data)

This self-contained book presents a framework for solving a general class of linear systems with coefficients being continuous functions of parameters varying within prescribed intervals. It also provides a comprehensive overview of the theory related to solving parametric interval linear systems and the basic properties of parametric interval matrices. In particular, it develops several new algorithms delivering sharp rigorous bounds for the solutions of such systems with full mathematical rigor. The framework employs the arithmetic of revised affine forms that enables the readers to handle dependent data. The book is intended not only for researchers interested in developing rigorous methods of numerical linear algebra, but also for engineers dealing with problems involving uncertain data. The theory discussed is also useful in various other fields of numerical analysis, in computer graphics, economics, computational geometry, computer-aided design, computer-assisted proofs, computer graphics, control theory, solving constraint satisfaction problems, and global optimization.
(source: Nielsen Book Data)

• Introduction to Granular Computing, Pattern Recognition and Data Mining.- Classification using Fuzzy Rough Granular Neural Networks.- Clustering using Fuzzy Rough Granular Self-Organizing Map.- Fuzzy Rough Granular Neural Network and Unsupervised Feature Selection.
• (source: Nielsen Book Data)

This book provides a uniform framework describing how fuzzy rough granular neural network technologies can be formulated and used in building efficient pattern recognition and mining models. It also discusses the formation of granules in the notion of both fuzzy and rough sets. Judicious integration in forming fuzzy-rough information granules based on lower approximate regions enables the network to determine the exactness in class shape as well as to handle the uncertainties arising from overlapping regions, resulting in efficient and speedy learning with enhanced performance. Layered network and self-organizing analysis maps, which have a strong potential in big data, are considered as basic modules, . The book is structured according to the major phases of a pattern recognition system (e.g., classification, clustering, and feature selection) with a balanced mixture of theory, algorithm, and application. It covers the latest findings as well as directions for future research, particularly highlighting bioinformatics applications. The book is recommended for both students and practitioners working in computer science, electrical engineering, data science, system design, pattern recognition, image analysis, neural computing, social network analysis, big data analytics, computational biology and soft computing.
(source: Nielsen Book Data)

Extract patterns and knowledge from your data in easy way using MATLAB About This Book * Get your first steps into machine learning with the help of this easy-to-follow guide * Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB * Understand how your data works and identify hidden layers in the data with the power of machine learning. Who This Book Is For This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well. What You Will Learn * Learn the introductory concepts of machine learning. * Discover different ways to transform data using SAS XPORT, import and export tools, * Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data. * Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment. * Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures. * Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox. * Learn feature selection and extraction for dimensionality reduction leading to improved performance. In Detail MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners. You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB. Style and approach The book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work.
(source: Nielsen Book Data)

• Introduction to fuzzy sets.- Introduction to fuzzy systems.- Ordered fuzzy numbers: sources and intuitions.- Ordered fuzzy numbers: definitions and operations.- Processing the direction with ordered fuzzy numbers.- Some cases of comparing fuzzy numbers using defuzzificators on the catalog of OFN shapes.- Two approaches to fuzzy implication.- The approach to capital budgeting under uncertainty and risk based on Ordered Fuzzy Numbers.- Input-Output Model Based on Ordered Fuzzy Numbers.- Ordered Fuzzy Candlesticks - financial data representation and analysis using Ordered Fuzzy Numbers.- Application of Ordered Fuzzy Numbers to detect trends in the Nasdaq composite index.- The OFNAnt method based on Ant Colony Optimization used to solve the TSP.- A new OFNBee method as an example of fuzzy observance applied for ABC optimization.- Fuzzy observation of DDoS attack.- Fuzzy control for secure TCP transfer.- Fuzzy Numbers applied to a heat furnace control.- Traditional vs OFN-based analysis of temporo-spatial gait parameters.- OFN-based brain function modeling.
• (source: Nielsen Book Data)

This book is open access under a CC BY 4.0 license. This open access book offers comprehensive coverage on Ordered Fuzzy Numbers, providing readers with both the basic information and the necessary expertise to use them in a variety of real-world applications. The respective chapters, written by leading researchers, discuss the main techniques and applications, together with the advantages and shortcomings of these tools in comparison to other fuzzy number representation models. Primarily intended for engineers and researchers in the field of fuzzy arithmetic, the book also offers a valuable source of basic information on fuzzy models and an easy-to-understand reference guide to their applications for advanced undergraduate students, operations researchers, modelers and managers alike.
(source: Nielsen Book Data)

• Computer Vision for Ocean Observing
• Fault Diagnosis and Classification of Mine Motor based on RS and SVM
• Particle Swarm Optimization based Image Enhancement of Visual Cryptography Shares
• Fast Level set Algorithm for Extraction and Evaluation of Weld Defects in Radiographic Images
• Efficient Combination of Color, Texture and Shape Descriptor, using SLIC Segmentation for Image Retrieval
• DEPO: Detecting Events of Public Opinion in Microblog
• Hybrid Cuckoo Search based Evolutionary Vector Quantization for Image Compression
• Edge and Fuzzy Transform based Image Compression Algorithm: edgeFuzzy
• Real-Time Implementation of Moving Object Action Recognition System Based on Motion Analysis
• Cross-modal Learning with Images, Texts and Their Semantics
• Light Field Vision for Artificial Intelligence
• Author Index.

This edited book presents essential findings in the research fields of artificial intelligence and computer vision, with a primary focus on new research ideas and results for mathematical problems involved in computer vision systems. The book provides an international forum for researchers to summarize the most recent developments and ideas in the field, with a special emphasis on the technical and observational results obtained in the past few years.

• Part I: Information Theoretic and Aggregation Issues.- Part II: Applications in Modeling, Decision Making, Control, and Other Areas.- Part III: Some Bibliometric Remarks.
• (source: Nielsen Book Data)

This book offers a comprehensive report on the state-of-the art in the broadly-intended field of "intelligent systems". After introducing key theoretical issues, it describes a number of promising models for data and system analysis, decision making, and control. It discusses important theories, including possibility theory, the Dempster-Shafer theory, the theory of approximate reasoning, as well as computing with words, together with novel applications in various areas, such as information aggregation and fusion, linguistic data summarization, participatory learning, systems modeling, and many others. By presenting the methods in their application contexts, the book shows how granular computing, soft computing and fuzzy logic techniques can provide novel, efficient solutions to real-world problems. It is dedicated to Professor Ronald R. Yager for his great scientific and scholarly achievements, and for his long-lasting service to the fuzzy logic, and the artificial and computational intelligence communities. It has been motivated by the authors' appreciation of his original thinking and groundbreaking ideas, with a special thought to his valuable research on the computerized implementation of various aspects of human cognition for decision-making and problem-solving. .
(source: Nielsen Book Data)

• Introduction
• Image pre-processing
• Main Image processing
• Additional Image Processing and Measurement
• Impact of Image Acquisition and Selection of Algorithm Parameters on the Results
• Summary of Measured Features
• Conclusions.

This monograph focuses on the use of analysis and processing methods for images from the Corvis® ST tonometer. The presented analysis is associated with the quantitative, repeatable and fully automatic evaluation of the response of the eye, eyeball and cornea to an air-puff. All the described algorithms were practically implemented in MATLAB®. The monograph also describes and provides the full source code designed to perform the discussed calculations. As a result, this monograph is intended for scientists, graduate students and students of computer science and bioengineering as well as doctors wishing to expand their knowledge of modern diagnostic methods assisted by various image analysis and processing methods.

• A Hybrid Intelligent Approach for Metal-Loss Defect Depth Prediction in Oil and Gas Pipelines.- Predicting Financial Time Series Data Using Hybrid Model.- Diagnosis System for Predicting Bladder Cancer Recurrence using Association Rules and Decision Trees.- An Unsupervised Text-Mining Approach and a Hybrid Methodology to Improve Early Warnings in Construction Project Management.- Quantitative Assessment of Anomaly Detection Algorithms in Annotated Datasets from the Maritime Domain.- Using Fuzzy PROMETHEE to Select Countries for Developmental Aid.- Self-Reconfiguring Robotic Framework Using Fuzzy and Ontological Decision Making.- Design and Optimization of Permanent Magnet Based Adhesion Module for Robots Climbing on Reinforced Concrete Surfaces.- Implementation of PID, Bang-bang and Backstepping con-trollers on 3D Printed Ambidextrous Robot Hand.- Multiple Robots Task Allocation for Cleaning and Delivery.- Optimal Tuning of Multivariable Centralized Fractional Order PID Controller using Bat Optimization and Harmony Search Algorithms for Two Interacting Conical Tank Process.- Entity Configuration and Context-Aware reasoNer (CAN) towards Enabling an Internet of Things Controller.- Track-Based Forecasting of Pedestrian Behavior by Polynomial Approximation and Multilayer Perceptrons.- Assembly Assisted by Augmented Reality (A3R).- Maximising Overlap Score in DNA Sequence Assembly Problem by Stochastic Diffusion Search.- A Comparative Analysis of Detecting Symmetries in Toroidal Topology.- Power Quality Enhancement in Off-Grid Hybrid Renewable Energy Systems Using Type-2 Fuzzy Control of Shunt Active Filter.- Fast Intra Mode Decision for HEVC.- WSN Efficient Data Management and Intelligent Communication for Load Balancing Based on Khalimsky Topology and Mobile Agents.- Indirect Method of Learning Weights Parameters of Intuitionistic Statement Networks.- Combined Data and Execution Flow Host Intrusion Detection using Machine Learning.
• (source: Nielsen Book Data)

This book is a remarkable collection of chapters covering a wider range of topics, including unsupervised text mining, anomaly and Intrusion Detection, Self-reconfiguring Robotics, application of Fuzzy Logic to development aid, Design and Optimization, Context-Aware Reasoning, DNA Sequence Assembly and Multilayer Perceptron Networks. The twenty-one chapters present extended results from the SAI Intelligent Systems Conference (IntelliSys) 2015 and have been selected based on high recommendations during IntelliSys 2015 review process. This book presents innovative research and development carried out presently in fields of knowledge representation and reasoning, machine learning, and particularly in intelligent systems in a more broad sense. It provides state - of - the - art intelligent methods and techniques for solving real world problems along with a vision of the future research.
(source: Nielsen Book Data)

• Introduction.- Describing Spatial Position and Orientation.- Robot Kinematics.- Joint Velocities and Jacobi-Matrices.- Navigation and Registration.- Treatment Planning.- Motion Correlation and Tracking.- Motion Prediction.- Motion Replication.- Applications of Surgical Robotics.- Rehabilitation, Neuroprosthetics and Brain-Machine Interfaces.
• (source: Nielsen Book Data)

This book provides a thorough background to the emerging field of medical robotics. It covers the mathematics needed to understand the use of robotic devices in medicine, including but not limited to robot kinematics, hand-eye and robot-world calibration, reconstruction, registration, motion planning, motion prediction, motion correlation, motion replication and motion learning. Additionally, basic methods behind state-of-the art robots like the DaVinci system, the CyberKnife, motorized C-arms and operating microscopes as well as stereotactic frames are presented. The book is a text book for undergraduates in computer science and engineering. The main idea of the book is to motivate the methods in robotics in medical applications rather than industrial applications. The book then follows the standard path for a robotics textbook. It is thus suitable for a first course in robotics for undergraduates. It is the first textbook on medical robotics.
(source: Nielsen Book Data)

• Cover ; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface;
• Chapter 1: Introducing Test-Driven Machine Learning; Test-driven development; The TDD cycle; Red; Green; Refactor; Behavior-driven development; Our first test; The anatomy of a test; Given; When; Then; TDD applied to machine learning; Dealing with randomness; Different approaches to validating the improved models; Classification overview; Regression; Clustering; Quantifying the classification models; Summary;
• Chapter 2: Perceptively Testing a Perceptron; Getting started; Summary

• Chapter 3: Exploring the Unknown with Multi-armed BanditsUnderstanding a bandit; Testing with simulation; Starting from scratch; Simulating real world situations; A randomized probability matching algorithm; A bootstrapping bandit; The problem with straight bootstrapping; Multi-armed armed bandit throw down; Summary;
• Chapter 4: Predicting Values with Regression; Refresher on advanced regression; Regression assumptions; Quantifying model quality; Generating our own data; Building the foundations of our model; Cross-validating our model; Generating data; Summary

• Chapter 5: Making Decisions Black and White with Logistic RegressionGenerating logistic data; Measuring model accuracy; Generating a more complex example; Test driving our model; Summary;
• Chapter 6: You're So Naïve, Bayes; Gaussian classification by hand; Beginning the development; Summary;
• Chapter 7: Optimizing by Choosing a New Algorithm; Upgrading the classifier; Applying our classifier; Upgrading to Random Forest; Summary;
• Chapter 8: Exploring Scikit-learn Test First; Test-driven design; Planning our journey

• Creating a classifier chooser (it needs to run tests to evaluate classifier performance)Getting choosey; Developing testable documentation; Decision trees; Summary;
• Chapter 9: Bringing It All Together; Starting at the highest level; The real world; What we've accomplished; Summary; Index

This book is intended for data technologists (scientists, analysts, or developers) with previous machine learning experience who are also comfortable reading code in Python. This book is ideal for those looking for a way to deliver results quickly to enable rapid iteration and improvement.
(source: Nielsen Book Data)

• Prologue 7 1. Introduction 10 1.1 Autonomous Systems 13 1.2 The Role of Machine Learning in Autonomous Systems 15 1.3 System Identification 20 1.3.3 Novelty detection, outliers and the link to structure innovation 20 1.4 On-line versus Off-line Identification 21 1.5 Adaptive and Evolving Systems 22 1.6 Evolving or Evolutionary Systems 23 1.7 Supervised versus Un-supervised Learning 25 1.8 Structure of the Book 26 PART I: Fundamentals 29 2. Fundamentals of Probability Theory 29 2.1 Randomness and Determinism 30 2.2 Frequentistic versus Belief-based Approach 33 2.3 Probability Densities and Moments 33 2.4 Density Estimation
• Kernel-based Approach 37 2.5 Recursive Density Estimation (RDE) 40 2.6 Detecting Novelties/Anomalies/Outliers using RDE 45 2.7 Conclusion 49 3. Fundamentals of Machine Learning and Pattern Recognition 51 3.1 Pre-processing 51 3.1.1 Normalisation and standardisation 52 3.1.2 Orthogonalization of inputs/features
• rPCA method 53 3.2 Clustering 56 3.2.1 Proximity measures and clusters shape 59 3.2.2 Off-line methods 60 3.2.3 Evolving clustering methods 65 3.3 Classification 73 3.3.1 Recursive LDA, rLDA 74 3.4 Conclusion 74 4. Fundamentals of Fuzzy Systems Theory 77 4.1 Fuzzy Sets 77 4.2 Fuzzy Systems, Fuzzy Rules 80 4.2.1 Fuzzy Systems of Zadeh-Mamdani Type 81 4.2.2 Takagi-Sugeno Fuzzy Systems 83 4.3 Fuzzy Systems with Non-parametric Antecedents (AnYa) 87 4.3.1 Architecture 87 4.3.2 Analysis of AnYa 90 4.4 FRB (Off-line) classifiers 91 4.5 Neuro-Fuzzy Systems 94 4.5.1 Neuro-fuzzy system architecture 94 4.5.2 Evolving NFS as a framework for autonomous learning and knowledge extraction from data streams 97 4.5.3 Linguistic interpretation of the NFS 98 4.6 State Space Perspective 99 4.7 Conclusions 100 Part II Methodology of Autonomous Learning Systems 101 5 Evolving System Structure from Streaming Data 101 5.1 Defining system structure based on prior knowledge 101 5.2 Data Space Partitioning 102 5.2.1 Regular partitioning of the data space 103 5.2.2 Data space partitioning through clustering 104 5.2.3. Data space partitioning based on data clouds 105 5.2.4. Importance of partitioning the joint input-output data space 105 5.2.5 Principles of data space partitioning for autonomous machine learning 107 5.2.6 Dynamic data space partitioning
• evolving system structure autonomously 108 5.3 Normalisation and Standardisation of Streaming Data in Evolving Environments 114 5.3.1 Standardization in an Evolving Environment 115 5.3.2 Normalisation in an Evolving Environment 116 5.4 Autonomous Monitoring of the Structure Quality 117 5.4.1 Autonomous Input Variables Selection 117 5.4.2 Autonomous Monitoring of the Age of the Local Sub-model 120 5.4.3 Autonomous Monitoring of the Utility of the Local Sub-model 122 5.4.4 Update of the Cluster Radii 123 5.5 Short- and Long-term Focal Points and Sub-models 124 5.6 Simplification and Interpretability Issues 125 5.7 Conclusion 127 6 Autonomous Learning Parameters of the Local Sub-models 129 6.1 Learning Parameters of Local Sub-models 130 6.2 Global versus Local Learning 131 6.3 Evolving Systems Structure Recursively 133 6.4 Learning Modes 137 6.5 Robustness to Outliers in Autonomous Learning 140 6.6 Conclusions 140 7 Autonomous Predictors, Estimators, Filters, Inferential Sensors 142 7.1 Predictors, Estimators, Filters
• Problem Formulation 142 7.2 Non-linear Regression 144 7.3 Time series 145 7.4 Autonomous Learning Sensors 146 7.4.1 Autonomous Sensors
• Problem Definition 146 7.4.2 A brief Overview of Soft/Intelligent/Inferential Sensors 147 7.4.3 Autonomous Intelligent Sensors (AutoSense) 149 7.4.4 AutoSense Architecture 151 7.4.5 Modes of Operation of AutoSense 152 7.4.6 Autonomous Input Variable Selection 152 7.5 Conclusions 153 8. Autonomous Learning Classifiers 155 8.1 Classifying data streams 155 8.2 Why adapt the classifier structure? 155 8.3 Architecture of Autonomous Classifiers of the Family AutoClassify 157 8.3.1 AutoClassify0 159 8.3.2 AutoClassify1 159 8.4 Learning AutoClassify from Streaming Data 162 8.4.1 Learning AutoClassify0 162 8.4.2 Learning AutoClassify1 163 8.5 Analysis of AutoClassify methods 163 8.6 Conclusions 164 9. Autonomous Learning Controllers 166 9.1 Indirect Adaptive Control Scheme 167 9.2 Evolving Inverse Plant Model from On-line Streaming Data 169 9.3 Evolving Fuzzy Controller Structure from On-line Streaming Data 170 9.4 Examples of using AutoControl 172 9.5 Conclusion 177 10. Collaborative Autonomous Learning Systems 179 10.1 Distributed Intelligence Scenarios 179 10.2 Autonomous Collaborative Learning 181 10.3 Collaborative Autonomous Clustering, AutoCluster by a team of ALSs 183 10.4 Collaborative Autonomous Predictors, Estimators, Filters and AutoSense by a team of ALSs 184 10.5 Collaborative Autonomous Classifiers AutoClassify by a team of ALSs 184 10.6 Superposition of Local Sub-models 185 10.7 Conclusion 186 PART III: Applications of ALS 187 11. Autonomous Learning Sensors for Chemical and Petro-chemical Industries 187 11.1 Case Study 1: Quality of the Products in an Oil Refinery 187 11.1.1 Introduction 187 11.1.2 The current state of the art 188 11.1.3 Problem description 189 11.1.4 The data set 189 11.1.5 AutoSesnse for kerosene quality prediction 191 11.1.6 AutoSense for Abel inflammability test 193 11.2 Case Study 2: Polypropylene Manufacturing 194 11.2.1 Problem description 194 11.2.2 Drift and shift detection by cluster age derivatives 198 11.2.3 Input variables selection 200 11.3 Conclusion 201 12. Autonomous Learning Systems in Mobile Robotics 203 12.1 The mobile robot Pioneer 3DX 203 12.2 Autonomous Classifier for Landmark Recognition 205 12.2.1 Corner detection and simple mapping of an indoor environment through wall following 207 12.2.2 Outdoor landmark detection based on visual input information 210 12.2.3 VideoDiaries 214 12.2.4 Collaborative scenario 217 12.3 Autonomous Leader Follower 220 12.4 Results Analysis 223 13. Autonomous Novelty Detection and Object Tracking in Video Streams 224 13.1 Problem Definition 224 13.2 Background subtraction and KDE for detecting visual novelties 225 13.2.1 Background subtraction method 225 13.2.2 Challenges 226 13.2.3 Parametric versus non-parametric approaches 229 13.2.4 Kernel Density Estimation method 230 13.3 Detecting Visual novelties with RDE Method 231 13.4 Object Identification in Image Frames using RDE 232 13.5 Real-time Tracking in Video Streams using ALS 234 13.6 Conclusion 237 14. Modelling Evolving User Behaviour with ALS 239 14.1 User Behaviour as an evolving phenomenon 239 14.2 Designing the User Behaviour Profile 241 14.3 Applying AutoClassify0 for modelling evolving user behaviour 244 14.4 Case studies 245 14.4.1 Users of UNIX commands 245 14.4.2 Modelling activity of people in a smart home environment 247 14.4.3 Automatic scene recognition 249 14.5 Conclusions 252 15. Epilogue 254 15.1 Conclusions 254 15.2 Open Problems 258 15.3 Future Directions 259 Bibliography 261 Index 274 Glossary 291 Appendices 295 A. Mathematical Foundations 296 A1 Probability distributions 297 A2 Basic matrix properties 300 B. Pseudo-code of the basic algorithms 302 B1 Mean shift with Epanechnikov kernel 302 B2 AutoCluster 304 B3 ELM 305 B4 AutoPredict 307 B5 AutoSense 308 B6 AutoClassify0 309 B7 AutoClassify1 311 B8 AutoControl 313.
• (source: Nielsen Book Data)

• Forewords xi Preface xix About the Author xxiii 1 Introduction 1 1.1 Autonomous Systems 3 1.2 The Role of Machine Learning in Autonomous Systems 4 1.3 System Identification an Abstract Model of the Real World 6 1.4 Online versus Offline Identification 9 1.5 Adaptive and Evolving Systems 10 1.6 Evolving or Evolutionary Systems 11 1.7 Supervised versus Unsupervised Learning 13 1.8 Structure of the Book 14 PART I FUNDAMENTALS 2 Fundamentals of Probability Theory 19 2.1 Randomness and Determinism 20 2.2 Frequentistic versus Belief-Based Approach 22 2.3 Probability Densities and Moments 23 2.4 Density Estimation Kernel-Based Approach 26 2.5 Recursive Density Estimation (RDE) 28 2.6 Detecting Novelties/Anomalies/Outliers using RDE 32 2.7 Conclusions 36 3 Fundamentals of Machine Learning and Pattern Recognition 37 3.1 Preprocessing 37 3.2 Clustering 42 3.3 Classification 56 3.4 Conclusions 58 4 Fundamentals of Fuzzy Systems Theory 61 4.1 Fuzzy Sets 61 4.2 Fuzzy Systems, Fuzzy Rules 64 4.3 Fuzzy Systems with Nonparametric Antecedents (AnYa) 69 4.4 FRB (Offline) Classifiers 73 4.5 Neurofuzzy Systems 75 4.6 State Space Perspective 79 4.7 Conclusions 81 PART II METHODOLOGY OF AUTONOMOUS LEARNING SYSTEMS 5 Evolving System Structure from Streaming Data 85 5.1 Defining System Structure Based on Prior Knowledge 85 5.2 Data Space Partitioning 86 5.3 Normalisation and Standardisation of Streaming Data in an Evolving Environment 96 5.4 Autonomous Monitoring of the Structure Quality 98 5.5 Short- and Long-Term Focal Points and Submodels 104 5.6 Simplification and Interpretability Issues 105 5.7 Conclusions 107 6 Autonomous Learning Parameters of the Local Submodels 109 6.1 Learning Parameters of Local Submodels 110 6.2 Global versus Local Learning 111 6.3 Evolving Systems Structure Recursively 113 6.4 Learning Modes 116 6.5 Robustness to Outliers in Autonomous Learning 118 6.6 Conclusions 118 7 Autonomous Predictors, Estimators, Filters, Inferential Sensors 121 7.1 Predictors, Estimators, Filters Problem Formulation 121 7.2 Nonlinear Regression 123 7.3 Time Series 124 7.4 Autonomous Learning Sensors 125 7.5 Conclusions 131 8 Autonomous Learning Classifiers 133 8.1 Classifying Data Streams 133 8.2 Why Adapt the Classifier Structure? 134 8.3 Architecture of Autonomous Classifiers of the Family AutoClassify 135 8.4 Learning AutoClassify from Streaming Data 139 8.5 Analysis of AutoClassify 140 8.6 Conclusions 140 9 Autonomous Learning Controllers 143 9.1 Indirect Adaptive Control Scheme 144 9.2 Evolving Inverse Plant Model from Online Streaming Data 145 9.3 Evolving Fuzzy Controller Structure from Online Streaming Data 147 9.4 Examples of Using AutoControl 148 9.5 Conclusions 153 10 Collaborative Autonomous Learning Systems 155 10.1 Distributed Intelligence Scenarios 155 10.2 Autonomous Collaborative Learning 157 10.3 Collaborative Autonomous Clustering, AutoCluster by a Team of ALSs 158 10.4 Collaborative Autonomous Predictors, Estimators, Filters and AutoSense by a Team of ALSs 159 10.5 Collaborative Autonomous Classifiers AutoClassify by a Team of ALSs 160 10.6 Superposition of Local Submodels 161 10.7 Conclusions 161 PART III APPLICATIONS OF ALS 11 Autonomous Learning Sensors for Chemical and Petrochemical Industries 165 11.1 Case Study
• 1: Quality of the Products in an Oil Refinery 165 11.2 Case Study
• 2: Polypropylene Manufacturing 172 11.3 Conclusions 178 12 Autonomous Learning Systems in Mobile Robotics 179 12.1 The Mobile Robot Pioneer 3DX 179 12.2 Autonomous Classifier for Landmark Recognition 180 12.3 Autonomous Leader Follower 193 12.4 Results Analysis 196 13 Autonomous Novelty Detection and Object Tracking in Video Streams 197 13.1 Problem Definition 197 13.2 Background Subtraction and KDE for Detecting Visual Novelties 198 13.3 Detecting Visual Novelties with the RDE Method 203 13.4 Object Identification in Image Frames Using RDE 204 13.5 Real-time Tracking in Video Streams Using ALS 206 13.6 Conclusions 209 14 Modelling Evolving User Behaviour with ALS 211 14.1 User Behaviour as an Evolving Phenomenon 211 14.2 Designing the User Behaviour Profile 212 14.3 Applying AutoClassify0 for Modelling Evolving User Behaviour 215 14.4 Case Studies 216 14.5 Conclusions 221 15 Epilogue 223 15.1 Conclusions 223 15.2 Open Problems 227 15.3 Future Directions 227 APPENDICES Appendix A Mathematical Foundations 231 Appendix B Pseudocode of the Basic Algorithms 235 References 245 Glossary 259 Index 263.
• (source: Nielsen Book Data)

Addresses the techniques and challenges of autonomous learning systems and illustrates the practical relevance of the approach with a wide range of applications Autonomous Learning Systems is the result of over a decade of focused research and studies in this emerging area which spans a number of well-known and well-established disciplines that include machine learning, system identification, data mining, fuzzy logic and fuzzy systems, neural networks, neuro-fuzzy systems, control theory and pattern recognition. The evolution of these systems has been both industry-driven with an increasing demand from sectors such as defence and security, aerospace and advanced process industries, bio-medicine and intelligent transportation, and research-driven - there is a strong trend of innovation of all of the above well-established research disciplines that is linked to their on-line and real-time application; their adaptability and flexibility. Providing an introduction to the key technologies, detailed technical explanations of the methodology, and an illustration of the practical relevance of the approach with a wide range of applications, this book addresses the challenges of autonomous learning systems with a systematic approach that lays the foundations for a fast growing area of research that will underpin a range of technological applications vital to both industry and society. Including a wide range of applications, illustrations and software code (MatLAB(r)) that can be used for further research it constitutes a valuable 'one-stop-shop' for academics, researchers, practicing engineers, computer specialists and defence and industry experts.
(source: Nielsen Book Data)
Autonomous Learning Systems is the result of over a decade of focused research and studies in this emerging area which spans a number of well-known and well-established disciplines that include machine learning, system identification, data mining, fuzzy logic, neural networks, neuro-fuzzy systems, control theory and pattern recognition. The evolution of these systems has been both industry-driven with an increasing demand from sectors such as defence and security, aerospace and advanced process industries, bio-medicine and intelligent transportation, as well as research-driven there is a strong trend of innovation of all of the above well-established research disciplines that is linked to their on-line and real-time application; their adaptability and flexibility. Providing an introduction to the key technologies, detailed technical explanations of the methodology, and an illustration of the practical relevance of the approach with a wide range of applications, this book addresses the challenges of autonomous learning systems with a systematic approach that lays the foundations for a fast growing area of research that will underpin a range of technological applications vital to both industry and society. Key features: * Presents the subject systematically from explaining the fundamentals to illustrating the proposed approach with numerous applications. * Covers a wide range of applications in fields including unmanned vehicles/robotics, oil refineries, chemical industry, evolving user behaviour and activity recognition. * Reviews traditional fields including clustering, classification, control, fault detection and anomaly detection, filtering and estimation through the prism of evolving and autonomously learning mechanisms. * Accompanied by a website hosting additional material, including the software toolbox and lecture notes. Autonomous Learning Systems provides a one-stop shop on the subject for academics, students, researchers and practicing engineers. It is also a valuable reference for Government agencies and software developers.
(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)

• THEORECTICAL UNDERPINNINGS OF COST-SENSTIVE MACHINE LEARNING: Algorithms for Active Learning. Semi-Supervised Learning: Some Recent Advances. Transfer Learning, Multi-Task Learning, and Cost-Sensitive Learning. Cost-Sensitive Cascades. Selective Data Acquisition for Machine Learning. COST-SENSITIVE MACHINE LEARNING APPLICATIONS: Minimizing Annotation Costs in Visual Category Learning. Reliability and Redundancy: Reducing Error Cost in Medical Imaging. Cost-Sensitive Learning in Computational Advertising. Cost-Sensitive Machine Learning for Information Retrieval. Index.
• (source: Nielsen Book Data)

• THEORECTICAL UNDERPINNINGS OF COST-SENSTIVE MACHINE LEARNING Algorithms for Active Learning, Burr SettlesQuery Strategy FrameworksA Unified View Summary and Outlook Semi-Supervised Learning: Some Recent Advances, Xueyuan Zhou, Ankan Saha, and Vikas SindhwaniSemi-Supervised Prediction for Structured OutputsTheoretical AnalysisNew Directions Transfer Learning, Multi-Task Learning, and Cost-Sensitive Learning, Bin Cao, Yu Zhang, and Qiang YangNotations Transfer Learning ModelsMulti-Task Learning ModelsConclusion and Future Work Cost-Sensitive Cascades, Vikas C. RaykarFeatures Incur a CostCascade of Classifiers Successful Applications of Cascaded ArchitecturesTraining a Cascade of ClassifiersTradeoff between Accuracy and Cost Conclusions and Future Work Selective Data Acquisition for Machine Learning, Josh Attenberg, Prem Melville, Foster Provost, and Maytal Saar-TsechanskyOverarching Principles for Selective Data AcquisitionActive Feature-Value Acquisition Labeling Features versus ExamplesDealing with Noisy AcquisitionPrediction Time Information AcquisitionAlternative Acquisition SettingsConclusion COST-SENSITIVE MACHINE LEARNING APPLICATIONS Minimizing Annotation Costs in Visual Category Learning, Sudheendra Vijayanarasimhan and Kristen GraumanReducing the Level of SupervisionReducing the Amount of SupervisionReducing the Effort Required in SupervisionCost-Sensitive Multi-Level Active LearningConclusion Reliability and Redundancy: Reducing Error Cost in Medical Imaging, X.S. Zhou, Y. Zhan, Z. Peng, M. Dewan, B. Jian, A. Krishnan, M. Harder, R. Schwarz, L. Lauer, H. Meyer, S. Grosskopf, U. Feuerlein, H. Ditt, and M. ScheueringA Measure of ReliabilityReliability of Pattern Localization: Asymmetric Cost for FPs and FNs Implications and Learning Strategy for Medical Imaging ApplicationsRelated Work and Discussions Cost-Sensitive Learning in Computational Advertising, Deepak AgarwalPerformance Advertising: Sponsored Search and Contextual MatchingDisplay AdvertisingDiscussion Cost-Sensitive Machine Learning for Information Retrieval, Martin Szummer and Filip RadlinskiUtility in Information RetrievalLearning to RankReducing Labeling CostMultiple Utilities Conclusion Index A Bibliography appears at the end of each chapter.
• (source: Nielsen Book Data)

In machine learning applications, practitioners must take into account the cost associated with the algorithm. These costs include: Cost of acquiring training data Cost of data annotation/labeling and cleaning Computational cost for model fitting, validation, and testing Cost of collecting features/attributes for test data Cost of user feedback collection Cost of incorrect prediction/classification Cost-Sensitive Machine Learning is one of the first books to provide an overview of the current research efforts and problems in this area. It discusses real-world applications that incorporate the cost of learning into the modeling process. The first part of the book presents the theoretical underpinnings of cost-sensitive machine learning. It describes well-established machine learning approaches for reducing data acquisition costs during training as well as approaches for reducing costs when systems must make predictions for new samples. The second part covers real-world applications that effectively trade off different types of costs. These applications not only use traditional machine learning approaches, but they also incorporate cutting-edge research that advances beyond the constraining assumptions by analyzing the application needs from first principles. Spurring further research on several open problems, this volume highlights the often implicit assumptions in machine learning techniques that were not fully understood in the past. The book also illustrates the commercial importance of cost-sensitive machine learning through its coverage of the rapid application developments made by leading companies and academic research labs.
(source: Nielsen Book Data)
In machine learning applications, practitioners must take into account the cost associated with the algorithm. These costs include: Cost of acquiring training dataCost of data annotation/labeling and cleaningComputational cost for model fitting, validation, and testingCost of collecting features/attributes for test dataCost of user feedback collect.
(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)

• ""Title""; ""Preface""; ""Organization""; ""Contents""; ""Special Session on Agents Behaviours for Artificial Markets""; ""Calibration of an Agent Based Model for Financial Markets""; ""Introduction""; ""The Farmer Joshi Model""; ""Data and Methodology""; ""Results, Comments and Further Research""; ""References""; ""A Multiagent System for Web-Based Risk Management in Small and Medium Business""; ""Introduction""; ""Application of Business Web Intelligence to Enterprise Risk Assessment and Management""; ""Multiagent Web System for Risk Management""; ""Results and Conclusions""; ""References""

• ""Drawing Technical Figures in Double Auction Artificial Stock Markets""""Introduction""; ""Description of the Artificial Stock Market""; ""The Fundamental and Chartist Investment Approaches""; ""The Bid and Ask Orders""; ""Simulation and Results""; ""Price Evolution and Technical Patterns""; ""Conclusions""; ""References""; ""Special Session on Multi-Agent Systems for Safety and Security""; ""VigilAgent Methodology: Modeling Normal and Anomalous Situations""; ""Introduction""; ""VigilAgent Methodology""; ""Case Study: Access Control""; ""System Specification""; ""Architectural Design""

• ""Detailed Design""""Implementation and Deployment""; ""Conclusions""; ""References""; ""Developing Secure Agent Infrastructures with Open Standards and Open-Source Technologies""; ""Introduction""; ""Magentix2 Overview""; ""Magentix2 Security Infrastructure""; ""Magentix2 Management Service""; ""Secure Agent Communication""; ""Conclusions""; ""References""; ""A Fingerprint-Based Secure Communications Method""; ""Introduction""; ""System Description""; ""Authentication""; ""Message Cyphering""; ""Coding of Exchanged Messages""; ""Cypher Key Generation Algorithm""

• ""Conclusions and Further Development""""References""; ""Special Session on Web Mining and Recommender Systems""; ""Multiagent System for Indexing and Retrieving Learning Objects""; ""Introduction""; ""Background""; ""The Proposed Model""; ""The Prototype""; ""The Validation Test""; ""Final Remarks""; ""References""; ""Data Mining Techniques for Web Page Classification""; ""Introduction to the Problem""; ""The Data: Features of Web Pages""; ""Transformed Data: Generation of the Object Attribute Table""; ""Classification Methods""; ""Decision Trees""; ""Conclusions and Future Work""

PAAMS, the International Conference on Practical Applications of Agents and Multi-Agent Systems is an evolution of the International Workshop on Practical Applications of Agents and Multi-Agent Systems. PAAMS is an international yearly tribune to present, to discuss, and to disseminate the latest developments and the most important outcomes related to real-world applications. It provides a unique opportunity to bring multi-disciplinary experts, academics and practitioners together to exchange their experience in the development of Agents and Multi-Agent Systems. This volume presents the papers that have been accepted for the 2011 edition in the special sessions: Special Session on Agents Behaviours for Artificial Markets, Special Session on Multi-Agent Systems for safety and securit, Special Session on Web Mining and Recommender Systems, Special Session on Adaptative Multi-Agent System, Special Session on Integration of Artificial Intelligence Technologies in Resource-Constrained Devices, Special Session on Bio-Inspired and Multi-Agents Systems: Applications to Languages and Special Session on Agents for smart mobility.

• 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 and FL 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)

• 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)

• Views on Evolvability of Embedded Systems; Foreword; Preface; Acknowledgements; Contents;
• Chapter 1 Researching Evolvability;
• Chapter 2 Architecting for Improved Evolvability;
• Chapter 3 Complementing Software Documentation;
• Chapter 4 Identifying and Investigating Evolution Type DecompositionWeaknesses;
• Chapter 5 Transferring Evolutionary Couplings to Industry;
• Chapter 6 An Execution Viewpoint Catalog for Software-Intensive and Embedded Systems;
• Chapter 7 Researching Reference Architectures Gerrit Muller;
• Chapter 8 A3 Architecture Overviews;
• Chapter 9 Linking Requirements and Implementation.

• Chapter 10 Workflow Modelling of Intended System Use
• Chapter 11 Supervisory Control Synthesis in the Medical Domain;
• Chapter 12 Creating High-Quality Behavioural Designs for Software-Intensive Systems;
• Chapter 13 Verifying Runtime Reconfiguration Requirements on UML Models;
• Chapter 14 Scheduling in MRI Scans processing;
• Chapter 15 Strategy-Focused Architecture Decision Making;
• Chapter 16 Balancing Time-to-Market and Quality in Evolving Embedded Systems;
• Chapter 17 Industrial Impact and Lessons Learned;
• Chapter 18 Conclusions; Annex; I Darwin Publications; II List of Darwin Partners; Index.

Evolvability, the ability to respond effectively to change, represents a major challenge to today's high-end embedded systems, such as those developed in the medical domain by Philips Healthcare. These systems are typically developed by multi-disciplinary teams, located around the world, and are in constant need of upgrading to provide new advanced features, to deal with obsolescence, and to exploit emerging enabling technologies. Despite the importance of evolvability for these types of systems, the field has received scant attention from the scientific and engineering communities. Views on Evolvability of Embedded Systems focuses on the topic of evolvability of embedded systems from an applied scientific perspective. In particular, the book describes results from the Darwin project that researched evolvability in the context of Magnetic Resonance Imaging (MRI) systems. This project applied the Industry-as-Laboratory paradigm, in which industry and academia join forces to ensure continuous knowledge and technology transfer during the project's lifetime. The Darwin project was a collaboration between the Embedded Systems Institute, the MRI business unit of Philips Healthcare, Philips Research, and five Dutch universities. Evolvability was addressed from a system engineering perspective by a number of researchers from different disciplines such as software-, electrical- and mechanical engineering, with a clear focus on economic decision making. The research focused on four areas: data mining, reference architectures, mechanisms and patterns for evolvability, in particular visualization & modelling, and economic decision making. Views on Evolvability of Embedded Systems is targeted at both researchers and practitioners; they will not only find a state-of-the-art overview on evolvability research, but also guidelines to make systems more evolvable and new industrially-validated techniques to improve the evolvability of embedded systems.

• 1. Foreword, pxi-xii
• 2. Acknowledgements, pxii
• 3. Contributors, pxiii-xxii
• 4. Section I. Setting the scene
• 5. In good company?: On the threshold of robotic Companions (by Turkle, Sherry), p3-10
• 6. Introducing artificial Companions (by Wilks, Yorick), p11-20
• 7. Section II. Ethical and philosophical issues
• 8. Artificial Companions and their philosophical challenges (by Floridi, Luciano), p23-28
• 9. Conditions for Companionhood (by Pulman, Stephen G.), p29-34
• 10. Arius in cyberspace: Digital Companions and the limits of the person (by O'--Hara, Kieron), p35-56
• 11. Section III. Social and psychological issues: What should a Companion be like?
• 12. Conversationalists and confidants (by Boden, Margaret A.), p59-61
• 13. Robots should be slaves (by Bryson, Joanna J.), p63-74
• 14. Wanting the impossible: The dilemma at the heart of intimate human-robot relationships (by Evans, Dylan), p75-88
• 15. Falling in love with a Companion (by Levy, David), p89-94
• 16. Identifying your accompanist (by Lowe, Will), p95-100
• 17. Look, emotion, language and behavior in a believable virtual Companion (by Romano, Daniela M.), p101-106
• 18. New Companions (by Taylor, Alex), p107-120
• 19. On being a Victorian Companion (by Wilks, Yorick), p121-128
• 20. Section IV. Design issues: Building a Companion
• 21. The use of affective and attentive cues in an empathic computer-based Companions (by Bee, Nikolaus), p131-142
• 22. GRETA: Towards an interactive conversational virtual Companion (by Bevacqua, Elisabetta), p143-156
• 23. A world-hybrid approach to a conversational Companion for reminiscing about images (by Catizone, Roberta), p157-168
• 24. Companionship is an emotional business (by Cowie, Roddy), p169-172
• 25. Artificial Companions in society: Consulting the users (by Newell, Alan), p173-178
• 26. Requirements for Artificial Companions: It'--s harder than you think (by Sloman, Aaron), p179-200
• 27. You really need to know what your bot(s) are thinking about you (by Winfield, Alan FT), p201-208
• 28. Section V. Special purpose Companions
• 29. A Companion for learning in everyday life (by Eynon, Rebecca), p211-220
• 30. The Maryland virtual patient as a task-oriented conversational Companion (by Nirenburg, Sergei), p221-244
• 31. Living with robots: Ethical tradeoffs in eldercare (by Sharkey, Noel), p245-256
• 32. Section VI. Afterword
• 33. Summary and discussion of the issues (by Peltu, Malcom), p259-286
• 34. References, p287-308
• 35. Index, p309-316.
• (source: Nielsen Book Data)

• 1. Foreword
• 2. Acknowledgements
• 3. Contributors
• 4. Section I. Setting the scene
• 5. In good company?: On the threshold of robotic Companions (by Turkle, Sherry)
• 6. Introducing artificial Companions (by Wilks, Yorick)
• 7. Section II. Ethical and philosophical issues
• 8. Artificial Companions and their philosophical challenges (by Floridi, Luciano)
• 9. Conditions for Companionhood (by Pulman, Stephen G.)
• 10. Arius in cyberspace: Digital Companions and the limits of the person (by O'Hara, Kieron)
• 11. Section III. Social and psychological issues: What should a Companion be like?
• 12. Conversationalists and confidants (by Boden, Margaret A.)
• 13. Robots should be slaves (by Bryson, Joanna J.)
• 14. Wanting the impossible: The dilemma at the heart of intimate human-robot relationships (by Evans, Dylan)
• 15. Falling in love with a Companion (by Levy, David)
• 16. Identifying your accompanist (by Lowe, Will)
• 17. Look, emotion, language and behavior in a believable virtual Companion (by Romano, Daniela M.)
• 18. New Companions (by Taylor, Alex)
• 19. On being a Victorian Companion (by Wilks, Yorick)
• 20. Section IV. Design issues: Building a Companion
• 21. The use of affective and attentive cues in an empathic computer-based Companions (by Bee, Nikolaus)
• 22. GRETA: Towards an interactive conversational virtual Companion (by Bevacqua, Elisabetta)
• 23. A world-hybrid approach to a conversational Companion for reminiscing about images (by Catizone, Roberta)
• 24. Companionship is an emotional business (by Cowie, Roddy)
• 25. Artificial Companions in society: Consulting the users (by Newell, Alan)
• 26. Requirements for Artificial Companions: It's harder than you think (by Sloman, Aaron)
• 27. You really need to know what your bot(s) are thinking about you (by Winfield, Alan FT)
• 28. Section V. Special purpose Companions
• 29. A Companion for learning in everyday life (by Eynon, Rebecca)
• 30. The Maryland virtual patient as a task-oriented conversational Companion (by Nirenburg, Sergei)
• 31. Living with robots: Ethical tradeoffs in eldercare (by Sharkey, Noel)
• 32. Section VI. Afterword
• 33. Summary and discussion of the issues (by Peltu, Malcom)
• 34. References
• 35. Index.
• (source: Nielsen Book Data)

What will it be like to admit Artificial Companions into our society? How will they change our relations with each other? How important will they be in the emotional and practical lives of their owners - since we know that people became emotionally dependent even on simple devices like the Tamagotchi? How much social life might they have in contacting each other? The contributors to this book discuss the possibility and desirability of some form of long-term computer Companions now being a certainty in the coming years. It is a good moment to consider, from a set of wide interdisciplinary perspectives, both how we shall construct them technically as well as their personal philosophical and social consequences. By Companions we mean conversationalists or confidants - not robots - but rather computer software agents whose function will be to get to know their owners over a long period. Those may well be elderly or lonely, and the contributions in the book focus not only on assistance via the internet (contacts, travel, doctors etc.) but also on providing company and Companionship, by offering aspects of real personalization.
(source: Nielsen Book Data)

• Acknowledgements Preface of Computer Epistemology 1991 Foreword
• Part 1 Why computer epistemology? 1.1 Prologue: why? 1.2 Knowledge about knowledge 1.3. Did it start also with the Greeks? 1.4. An important addendum about the not formalized human issue
• Part 2 Algebra, the discipline from the simplest to the most general 2.1 Introduction to the Game of Life and thinking 2.2 Algebra, the ladder from counting to coordinatizating the universe 2.3 Sets, other entity abstractions 2.4 Algebraic operations in highly practical roles: computational classes 2.5 Two examples of application algebraic methods 2.6 Abstracted reality: reflections in the brain and resume
• Part 3 Logic, the origin of all programming 3.1 Basic problems 3.2 Logic in computers, now 3.3 About final truth, epistemic ethics
• Part 4 How uncertain uncertainty is? 4.1 Long story of uncertainty about uncertainty 4.2 Late evolution 4.3 The pragmatic view of methodologies
• Part 5 Excursion to the fields of ontology, Being and beliefs 5.1 Ontology, homunculus, constructive skepticism 5.2 Ethics: our pragma: useful and necessary 5.3 Analytic versus metaphysical, logic versus pattern 5.4 Future human roles and attitudes and constructive skepticism
• Part 6 Conclusions Appendices References Name index Subject index.
• (source: Nielsen Book Data)

The result of the author's extensive practical experience: a decade in computer process control using large scale systems, another decade in machine pattern-recognition for vision systems, and nearly a decade dealing with artificial intelligence and expert systems. These real-life projects have taught Vamos a critical appreciation of, and respect for, both abstract theory and the practical methodology that grows out of-and, in turn, shapes-those theories. Machine representation means a level of formalization that can be expressed by the instruments of mathematics, whereas programming is not more and not less than a special linguistic translation of these mathematical formulae. How these all are related and controlled is a most practical philosophical and computation professional task. Wide experience in the practical fields of computer science, and the research of the underlying theoretical issues have led Vamos to the development of the attitude and activity of constructive skepticism.
(source: Nielsen Book Data)

• Acknowledgements Preface of Computer Epistemology 1991 Foreword
• Part 1 Why computer epistemology? 1.1 Prologue: why? 1.2 Knowledge about knowledge 1.3. Did it start also with the Greeks? 1.4. An important addendum about the not formalized human issue
• Part 2 Algebra, the discipline from the simplest to the most general 2.1 Introduction to the Game of Life and thinking 2.2 Algebra, the ladder from counting to coordinatizating the universe 2.3 Sets, other entity abstractions 2.4 Algebraic operations in highly practical roles: computational classes 2.5 Two examples of application algebraic methods 2.6 Abstracted reality: reflections in the brain and resume
• Part 3 Logic, the origin of all programming 3.1 Basic problems 3.2 Logic in computers, now 3.3 About final truth, epistemic ethics
• Part 4 How uncertain uncertainty is? 4.1 Long story of uncertainty about uncertainty 4.2 Late evolution 4.3 The pragmatic view of methodologies
• Part 5 Excursion to the fields of ontology, Being and beliefs 5.1 Ontology, homunculus, constructive skepticism 5.2 Ethics: our pragma: useful and necessary 5.3 Analytic versus metaphysical, logic versus pattern 5.4 Future human roles and attitudes and constructive skepticism
• Part 6 Conclusions Appendices References Name index Subject index.
• (source: Nielsen Book Data)

The result of the author's extensive practical experience: a decade in computer process control using large scale systems, another decade in machine pattern-recognition for vision systems, and nearly a decade dealing with artificial intelligence and expert systems. These real-life projects have taught Vamos a critical appreciation of, and respect for, both abstract theory and the practical methodology that grows out of-and, in turn, shapes-those theories. Machine representation means a level of formalization that can be expressed by the instruments of mathematics, whereas programming is not more and not less than a special linguistic translation of these mathematical formulae. How these all are related and controlled is a most practical philosophical and computation professional task. Wide experience in the practical fields of computer science, and the research of the underlying theoretical issues have led Vamos to the development of the attitude and activity of constructive skepticism.
(source: Nielsen Book Data)

• Title page; Preface; Contents; Towards Ontology Use, Re-Use and Abuse in a Computational Creativity Collective; Ontology Modularity, Information Flow, and Interaction-Situated Semantics; The Modular Structure of an Ontology: An Empirical Study; Extracting and Merging Contextualized Ontology Modules; A Metric Suite for Evaluating Cohesion and Coupling in Modular Ontologies; Towards a Functional Approach to Modular Ontologies Using Institutions; Introducing Ontology Best Practices and Design Patterns into Robotics: USAREnv; Modular Upper-Level Ontologies for Semantic Complex Event Processing.

Modularity, in its different shapes and forms, remains one of the central research topics in ontology engineering, still catching up with 40 years of related research in software engineering. The workshops on Modular Ontologies (WoMO) bring together researchers from different disciplines who study the problem of modularity in ontologies at a fundamental level, develop design tools for distributed ontology engineering and apply modularity in different use cases and application scenarios. The contributions in this volume are of interest to researchers, students and practitioners interested in Se.

• Soft Computing for Modeling, Control, and Optimization.- Optimization of Co-rotating Twin-Screw Extruders Using Pareto Local Search.- Efficient Predictive Control Algorithm Based on Fuzzy Hammerstein Models: A Case Study.- Selection of Points Inside Cutoff Radius by Scanning All Points Sorted in Memory.- Supervised Fuzzy Clustering Based Initialization of Fuzzy Partitions for Decision Tree Induction.- Fault Detection of the Tennessee Eastman Process Using Improved PCA and Neural Classifier.- New Evolutionary Methodologies.- Bare Bones Particle Swarm Applied to Parameter Estimation of Mixed Weibull Distribution.- Structural Optimization Using Harmony Search Algorithm.- Nash Dominance with Applications to Equilibrium Problems with Equilibrium Constraints.- A New Evolutionary Based Approach for Solving the Uncapacitated Multiple Allocation p-Hub Median Problem.- Soft Computing for Bioinformatics, Knowledge Management, and Data Mining.- Prediction of the hERG Potassium Channel Inhibition Potential with Use of the Artificial Neural Networks.- Michigan Style Fuzzy Classification for Gene Expression Analysis.- Feature Selection for Bankruptcy Prediction: A Multi-Objective Optimization Approach.- Dynamic Time Warping of Segmented Time Series.- Feature Selection Using Combine of Genetic Algorithm and Ant Colony Optimization.- Soft Computing for Image and Signal Processing, and Pattern Recognition.- Fully Automatic Boundary Extraction of Coronary Plaque in IVUS Image by Anisotropic Diffusion and T-S Type Fuzzy Inference.- Cost Effective 3D Localization for Low-Power WSN Based on Modified Sammon's Stress Function with Incomplete Distance Information.- Classification of Pressure Drop Devices of Proto Type Fast Breeder Reactor through Seven Layered Feed Forward Neural Network.- Music Visualization by Means of Comb Filter and Relaxation Time According to Human Perception.- GA/SVM for Diagnosis Sleep Stages Using Non-linear and Spectral Features.- Special Session Soft Computing in Computer Graphics, Imaging, and Vision.- Prostate Boundary Detection in Ultrasound Images Based on Type-II Fuzzy Sets and Modified Fuzzy C-Means.- Distributed Multimedia Computing Using Intelligent Agents.- Object Recognition Using Simulated Evolution on Fourier Descriptors.- Special Session Soft Computing for Intelligent Control.- Embedding a KM Type Reducer for High Speed Fuzzy Controller into an FPGA.- An Application of Fuzzy Lyapunov Synthesis in the Design of Type-2 Fuzzy Logic Controllers.- Access Control and Resource Allocation for Peer-to-Peer Video Streaming Based on Fuzzy Logic.- Bio-inspired Optimization Methods of Fuzzy Logic Controllers Applied to Linear Plants.- Special Session Traffic and Transportation Systems.- An Adaptive Neuro-Fuzzy Inference System for Simulation of Pedestrians Behaviour at Unsignalized Roadway Crossings.- A Cellular Genetic Algorithm for Solving the Vehicle Routing Problem with Time Windows.- Fuzzy C-Means Based Stop Line and Crosswalk Placement at Right Turn Vehicle Exclusive Lane.- Surveys and Tutorials.- New Trends of Soft Computing Methods for Industrial and Biological Processes.- Evolutionary Multi-objective Optimisation of Business Processes.- Computational Intelligence in Speech and Audio Processing: Recent Advances.
• (source: Nielsen Book Data)

The 14th online World Conference on Soft Computing in Industrial Applications provides a unique opportunity for soft computing researchers and practitioners to publish high quality papers and discuss research issues in detail without incurring a huge cost. The conference has established itself as a truly global event on the Internet. The quality of the conference has improved over the years. The WSC14 conference has covered new trends in soft computing to state of the art applications. The conference has also added new features such as community tools, syndication, and multimedia online presentations.
(source: Nielsen Book Data)

• Special Session on Adaptive Multiagent Systems.- Use of Agents to Realize a Federated Searching of Learning Objects.- MDD for Virtual Organization Design.- Towards Soccer Simulation as a Testbed for Adaptive Systems and Agreement Technologies.- Normative Argumentation.- Engineering Ambient Intelligence Services by Means of MABS.- Mobile Agent Middleware for Intelligent Management of Communication Infrastructures.- A Multiagent System for Efficient Portfolio Management.- Managing Real-Time Web Services through Agents.- Open MAS Architecture. Providing Real Time Solutions.- Special Session on Multiagent Systems for Health Care and Bioinformatics.- PathAgent: Multi-agent System for Updated Pathway Information Integration.- Multi-agent System for Mass Spectrometry Analysis.- Multi-agent Personal Memory Assistant.- TENSSION: A Tool for the Medical Patient Follow-Up.- Dynamic Planning with Bayesian Network Applied in MAS.- Special Session on Multiagent Systems for Ambient Intelligence.- Enhancing the Role of Multi-agent Systems in the Development of Intelligent Environments.- Towards Distributed Wireless Intelligent Sensor Networks.- Context-Aware Agents for Vehicular Networks: An Aspect-Oriented Approach.- Reasoning on a Semantic Web Based Context-Awareness Middleware.- Agent-Based AmI System Case Study: The Easy Line + Project.- Multiagent System for Detecting Passive Students in Problem-Based Learning.- Context-Aware Agents for People Detection and Stereoscopic Analysis.- Analysis and Design of a SOA-Based Multi-agent Architecture.- Special Session on Multi-Agent Systems for Manufacturing and Supply Chain.- A Multiagent Scheduling Repair Method for Disruption Management in Complex Socio-technical Organizations.- Price Updating in Combinatorial Auctions for Coordination of Manufacturing Multiagent Systems.- Study on Integrated Model of Lean and Agile Supply Chain Based on Multi-DPs.- Towards Improving Supply Chain Coordination through Agent-Based Simulation.- Using Multi-agent System for Improving and Implementing a New Enterprise Modeling Tool.- Production Process Based on CIMOSA Modeling Approach and Software Agents.- A Beehive-Like Approach for Dynamic Generation of Integration Services to Enable Adaptive Supply Chains Using Enterprise Tomography.- Cooperative Purchasing of Logistics Services among Manufacturing Companies Based on Semantic Web and Multi-agent System.- Sandbox for Development of Evolving Manufacturing Control Architectures.- Agent-Based Model of Kanban Flows in the Environment with High Demand Variances.- Supply Chain with Reverse Flows- Benefiting from Recycled Resources.- Special Session on Enterprise Application and Information Integration.- e-Wedding Based on Multi-agent System.- Distributed Computing Using RESTful Semantic Web Services.- Enhancing the Services Integration Mechanism in the HoCa Multi-agent Architecture.- Integrating Deep-Web Information Sources.- Ontological Trading in a Multi-agent System.- An Agent-, Service-Based Platform Supporting Ontological Integration of Proficient Knowledge Managed in Decision Making.- Special Session on Software Agents in Knowledge Management.- A Multi-agent Recommender System to Suggest Documents in Communities of Practice.- AIDeM: Agent-Based Intrusion Detection Mechanism.- A Multi-agent System that Searches for Learning Objects in Heterogeneous Repositories.- Application of a Modeling Standard Language on the Definition of Agent Oriented Development Processes.- Special Session on Bio-Inspired and Multi-Agents Systems: Applications to Languages.- Variable Length-Based Genetic Representation to Automatically Evolve Wrappers.- A Case Study on Grammatical-Based Representation for Regular Expression Evolution.- Towards the Automatic Programming of H Systems: jHsys, a Java H System Simulator.- L Systems as Bio-MAS for Natural Language Processing.- Poetic RNA: Adapting RNA Design Methods to the Analysis of Poetry.- A Visual Language for Modelling and Simulation of Networks of Evolutionary Processors.- An Evolutionary Confidence Measure for Spotting Words in Speech Recognition.- Workshop on Artificial Intelligence and Distributed Systems.- Intelligent Agents and Wireless Sensor Networks: A Healthcare Telemonitoring System.- Indoor Navigation Multi-agent System for the Elderly and People with Disabilities.- Algorithms of Machine Learning for K-Clustering.- Monitoring and Adaptation of Assessment Process in Virtual Courses Based in Multi-agent Systems.- A Model-Based Ambient Agent Providing Support in Handling Desire and Temptation.- Cluster Analysis and Decision Trees of MR Imaging in Patients Suffering Alzheimer's.- Improving the Quality of Health Service with Smart Communication.- Multi-agent System (MAS) Applications in Ambient Intelligence (AmI) Environments.- Agents to Help Context-Aware System in Home Care.- SDL Ontology for Specifying Systems Based on Finite State Machines.- Applying Data Mining Techniques to Stock Market Analysis.- Synthesis and Analysis of Classifiers Based on Generalized Model of Identification.- Multiagent Based Spectrum Sharing Using Petri Nets.- A Multi Agent Architecture for Tourism Recommendation.- Improving Functionalities in a Multi-agent Architecture for Ocean Monitoring.- Multiagent Systems in Stock Index Prediction.- Experiments on Robotic Multi-agent System for Hose Deployment and Transportation.- On Cooperative Swarm Foraging for Simple, Non Explicitly Connected, Agents.- Analyzing Large-Scale Crowd Simulations for Building Evacuation.- Agent Collaboration Framework.- Workshop on Systems, Man, & Cybernetics: SMC-Workshop.- IEEE.- SPANISH CHAPTER.- Some Issues and Extensions of JADE to Cope with Multi-agent Operation in the Context of Ambient Intelligence.- P300-Based Brain-Computer Interface for Internet Browsing.- Modeling Virtual Agent Behavior in a Computer Game to Be Used in a Real Enviroment.- A Legal View of Ambient Assisted Living Developments.- An Intelligent Tutoring System Oriented to the Integration of People with Intellectual Disabilities.- An Automatic Programming ACO-Based Algorithm for Classification Rule Mining.- Energy Cost Reduction in the Synchronization of a Pair of Nonidentical Coupled Hindmarsh-Rose Neurons.- A Color Transformation for Robust Detection of Color Landmarks in Robotic Contexts.- Low Quality Data Management for Optimising Energy Efficiency in Distributed Agents.- Interactive Multimedia Tabletops (IMT) for Casual Users.- Emotion Elicitation Oriented to the Development of a Human Emotion Management System for People with Intellectual Disabilities.- Acoustic Phonetic Decoding Oriented to Multilingual Speech Recognition in the Basque Context.- A Computer-Aided Decision Support System for Shoulder Pain Pathology.- Unsupervised Visualization of SQL Attacks by Means of the SCMAS Architecture.- Assessing Knowledge Management in the Power Sector through a Connectionist Model.
• (source: Nielsen Book Data)

PAAMS, the International Conference on Practical Applications of Agents and Multi-Agent Systems is an international yearly forum to present, to discuss, and to disseminate the latest developments and the most important outcomes related to real-world applications. It provides a unique opportunity to bring multi-disciplinary experts, academics and practitioners together to exchange their experience in the development of Agents and Multi-Agent Systems. This volume presents the papers that have been accepted for the 2010 edition in the Special Sessions and Workshops. PAAMS'10 Special Sessions and Workshops are a very useful tool in order to complement the regular program with new or emerging topics of particular interest to the participating community. Special Sessions and Workshops that emphasize on multi-disciplinary and transversal aspects, as well as cutting-edge topics were especially encouraged and welcomed.
(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)