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

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

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

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

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

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

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

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

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

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

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

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

• Consequence
• Logics
• Classical Belief Revision
• AGM Contraction in Non-Classical Logics
• AGM Revision in Logics Without Negation
• Base Revision in Logics Without Negation
• Algorithms for Belief Bases
• Conclusion.

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

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

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

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

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

• Introduction to the subject of an agent in computer science.- Agent versus decomposition of an algorithm.- M-agent.- The agent system for balancing the distribution of resources.- The examples of applications of the agent systems.- Conclusion. Introduction to the subject of an agent in computer science.- Agent versus decomposition of an algorithm.- M-agent.- The agent system for balancing the distribution of resources.- The examples of applications of the agent systems.- Conclusion.
• (source: Nielsen Book Data)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Search engines, with Google at the top, have become the most heavily used online service, with millions of searches performed every day and many remarkable capabilities. Soft Computing for Information Processing and Analysis includes reports from the front of soft computing in the internet industry and imparts knowledge and understanding of the significance of the field's accomplishments, new developments and future directions. This carefully edited book has evolved from presentations made by the participants of a meeting entitled "Fuzzy Logic and the Internet: Enhancing the Power of the Internet", organized by the Berkeley Initiative in Soft Computing (BISC), University of California, Berkeley. It addresses the important topics of modern search engines such as fuzzy query, decision analysis and support systems, including articles about topics such as Web Intelligence, World Knowledge and Fuzzy Logic (by Lotfi A. Zadeh), perception based information processing, or web intelligence.
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For centuries, people have been fascinated by the possibility of building an artificial system that behaves intelligently. Now there is a new entry in this arena - neural networks. Naturally Intelligent Systems offers a comprehensive introduction to these exciting systems. It provides a technically accurate, yet down-to-earth discussion of neural networks, clearly explaining the underlying concepts of key neural network designs, how they are trained, and why they work. Throughout, the authors present actual applications that illustrate neural networks' utility in the new world.
(source: Nielsen Book Data)
Naturally Intelligent Systems offers a comprehensive introduction to neural networks.
(source: Nielsen Book Data)
For centuries, people have been fascinated by the possibility of building an artificial system that behaves intelligently. From Mary Shelley's Frankenstein monster to the computer intelligence of HAL in 2001, scientists have been cast in the role of creator of such devices. Now there is a new entry into this arena, neural networks, and "Naturally Intelligent Systems explores these systems to see how they work and what they can do. Neural networks are not computers in any traditional sense, and they have little in common with earlier approaches to the problem of fabricating intelligent behavior. Instead, they are information processing systems that are physically modeled after the structure of the brain and that are "trained to perform a task rather than programmed like a computer. Neural networks, in fact, provide a tool with problemsolving capabilities - and limitations - strikingly similar to those of animals and people. In particular, they are successful in applications such as speech, vision, robotics, and pattern recognition. "Naturally Intelligent Systems offers a comprehensive introduction to these exciting systems. It provides a technically accurate, yet down-to-earth discussion of neural networks. No particular mathematical background is necessary; it is written for all interested readers. "Naturally Intelligent Systents clearly explains the underlying concepts of key neural network designs, how they are trained, and why they work. It compares their behavior to the natural intelligence found in animals - and people. Throughout, Caudill and Butler bring the field into focus by presenting actual applications that illustrate neural networks' utility in the real world. MaureenCaudill is President of Adaptics, a neural network consulting company in San Diego and author of the popular "Neural Network Primer" articles that appear regularly in "AI Expert. Charles Butler is a Senior Principal Scientist at Physical Sciences in Alexandria, Virginia. He is a specialist in neural network application development. A Bradford Book.
(source: Nielsen Book Data)

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

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

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

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

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

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

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

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

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

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

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

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

Logic and its components (propositional, first-order, non-classical) play a key role in Computer Science and Artificial Intelligence. While a large amount of information exists scattered throughout various media (books, journal articles, webpages, etc.), the diffuse nature of these sources is problematic and logic as a topic benefits from a unified approach. Logic for Computer Science and Artificial Intelligence utilizes this format, surveying the tableaux, resolution, Davis and Putnam methods, logic programming, as well as for example unification and subsumption. For non-classical logics, the translation method is detailed. Logic for Computer Science and Artificial Intelligence is the classroom-tested result of several years of teaching at Grenoble INP (Ensimag). It is conceived to allow self-instruction for a beginner with basic knowledge in Mathematics and Computer Science, but is also highly suitable for use in traditional courses. The reader is guided by clearly motivated concepts, introductions, historical remarks, side notes concerning connections with other disciplines, and numerous exercises, complete with detailed solutions, The title provides the reader with the tools needed to arrive naturally at practical implementations of the concepts and techniques discussed, allowing for the design of algorithms to solve problems.
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• Investigating Universal Computability of Conventional Cellular Automata Problems on an Organic Molecular Matrix Noise-Based Logic and Computing: From Boolean Logic Gates to Brain Circuitry and Its Possible Hardware Realization Models and Mechanisms for Artificial Morphogenesis Biologically-Inspired Network Architecture for Future Networks Foraging Behaviors and Potential Computational Ability of Problem-Solving in an Amoeba Two Molecular Information Processing Systems Based on Catalytic Nucleic Acids The Effect of Community on Distributed Bio-inspired Service Composition Efficient Computation in Brownian Cellular Automata A Molecular Communication System Properties of Threshold Coupled Chaotic Neuronal Maps Implementation of Rotary Element with Quantum Cellular Automata Universal 2-State Asynchronous Cellular Automaton with Inner-Independent Transitions Effect of Population Size in Extended Parameter-Free Genetic Algorithm Temperature Effects on Olive Fruit Fly Infestation in the FlySim Cellular Automata Model Computing by Observing Changes Robustness of the Critical Behaviour in a Discrete Stochastic Reaction-Diffusion Medium Quantifying the Severity of the Permutation Problem in Neuroevolution Extending the Geometrical Design of DNA Nanostructures An Optical Solution for the Subset Sum Problem Design of True Random One-Time Pads in DNA XOR Cryptosystem On Designing Gliders in Three-Dimensional Larger than Life Cellular Automata Instability of Collective Flow in Two-Dimensional Optimal Velocity Model A New Differential Evolution for Multiobjective Optimization by Uniform Design and Minimum Reduce Hypervolume Noise Effects on Chaos in Chaotic Neuron Model Application of Improved Grammatical Evolution to Santa Fe Trail Problems Limit Theorem for a Time-Dependent Coined Quantum Walk onthe Line Top-Predator Survivor Region Is Affected by Bottom-Prey Mortality Rate on the Monte-Carlo Simulation in Lattice Model Simulation and Theoretical Comparison between 'Zipper' and 'Non-Zipper' Merging Universality of 2-State 3-Symbol Reversible Logic Elements - A Direct Simulation Method of a Rotary Element Pump Current as a Signal Transformation Evaluation of Generation Alternation Models in Evolutionary Robotics Photonic Switching of DNA's Position That Represents the Internal State in Photonic DNA Automaton Fluctuation Induced Structure in Chemical Reaction with Small Number of Molecules Parallel Retrieval of Nanometer-Scale Light-Matter Interactions for Nanophotonic Systems A Compressible Fluid Model for Traffic Flow and Nonlinear Saturation of Perturbation Growth Functional Sized Population Magnetic Optimization Algorithm Emergence and Collapse of Order in Ad Hoc Cellular Automata A Transition Rule Set for the First 2-D Optimum-Time Synchronization Algorithm A Two-Dimensional Optimum-Time Firing Squad Synchronization Algorithm and Its Implementation Quaternion Based Thermal Condition Monitoring System Firing Correlation in Spiking Neurons with Watts-Strogatz Rewiring Methods for Shortening Waiting Time in Walking-Distance Introduced Queueing Systems Effect of Mutation to Distribution of Optimum Solution in Genetic Algorithm.
• (source: Nielsen Book Data)

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

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

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

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

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

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

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

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

• Part I: Type-2 Fuzzy Logic in Metaheuristics.- A comparative study of dynamic adaptation of parameters in the GWO algorithm using type-1 and interval type-2 fuzzy logic.- Ensemble Neural Network optimization using a gravitational search algorithm with Interval Type-1 and Type-2 fuzzy parameter adaptation in pattern recognition applications.- Improved method based on type-2 fuzzy logic for the adaptive harmony search algorithm.- Comparison of bio-inspired methods with parameter adaptation through interval type-2 fuzzy logic.- Differential Evolution algorithm with Interval type-2 fuzzy logic for the optimization of the mutation parameter.- Part II: Neural Networks Theory and Applications.- Person recognition with modular deep neural network using the iris biometric measure.- Neuro-evolutionary Neural Network for the Estimation of Melting Point of Ionic Liquids.- A proposal to classify ways of walking patterns using spik-ing neural networks.- Partially-connected Artificial Neural Networks developed by Grammatical Evolution for pattern recognition problems.- Part III: Metaheuristics: Theory and Applications.- Bio-inspired Metaheuristics for Hyper-parameter Tuning of Support Vector Machine Classifiers.
• (source: Nielsen Book Data)

This book comprises papers on diverse aspects of fuzzy logic, neural networks, and nature-inspired optimization meta-heuristics and their application in various areas such as intelligent control and robotics, pattern recognition, medical diagnosis, time series prediction and optimization of complex problems. The book is organized into seven main parts, each with a collection of papers on a similar subject. The first part presents new concepts and algorithms based on type-2 fuzzy logic for dynamic parameter adaptation in meta-heuristics. The second part discusses network theory and applications, and includes papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The third part addresses the theory and practice of meta-heuristics in different areas of application, while the fourth part describes diverse fuzzy logic applications in the control area, which can be considered as intelligent controllers. The next two parts explore applications in areas, such as time series prediction, and pattern recognition and new optimization and evolutionary algorithms and their applications respectively. Lastly, the seventh part addresses the design and application of different hybrid intelligent systems.
(source: Nielsen Book Data)

Use Java to create a diverse range of Data Science applications and bring Data Science into production About This Book * An overview of modern Data Science and Machine Learning libraries available in Java * Coverage of a broad set of topics, going from the basics of Machine Learning to Deep Learning and Big Data frameworks. * Easy-to-follow illustrations and the running example of building a search engine. Who This Book Is For This book is intended for software engineers who are comfortable with developing Java applications and are familiar with the basic concepts of data science. Additionally, it will also be useful for data scientists who do not yet know Java but want or need to learn it. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing the existing stack, this book is for you! What You Will Learn * Get a solid understanding of the data processing toolbox available in Java * Explore the data science ecosystem available in Java * Find out how to approach different machine learning problems with Java * Process unstructured information such as natural language text or images * Create your own search engine * Get state-of-the-art performance with XGBoost * Learn how to build deep neural networks with DeepLearning4j * Build applications that scale and process large amounts of data * Deploy data science models to production and evaluate their performance In Detail Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises. Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort. This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data. Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings. Style and approach This is a practical guide where all the important concepts such as classification, regression, and dimensionality reduction are explained with the help of examples.
(source: Nielsen Book Data)

• Intro; Preface; Organization; Contents; Utilizing Permission Norms in BDI Practical Normative Reasoning; 1 Introduction; 2 Background; 2.1 Jason; 2.2 Event Calculus; 3 Norm Representation; 3.1 Norms; 4 BDI Agent Normative Reasoning; 5 Experiments; 5.1 Gold and Silver Mining Society; 6 Summary; References; Holonic Institutions for Multi-scale Polycentric Self-governance; 1 Introduction; 2 Background and Motivation; 2.1 Formalising Ostrom's Principles; 2.2 The Eighth Principle: Nested Enterprises; 2.3 An Example; 3 Institutions and Holons; 3.1 Institutions: An Informal Overview

• 3.2 Self-organising Electronic Institutions3.3 Holonic Systems; 4 Holonic Institutions; 5 Case Study for Community Energy Systems; 5.1 Smart Houses; 5.2 Decentralised Community Energy Systems; 5.3 Holonic Institutions; 5.4 Polycentric Self-Governance; 6 Related and Future Work; 7 Summary and Conclusions; References; Contextualized Planning Using Social Practices; 1 Introduction; 2 Background; 2.1 2APL; 2.2 Case-Based Reasoning; 2.3 Work Practice Simulation; 3 Social Practices; 3.1 Social Intelligence and Social Practices; 3.2 Characteristics of Social Practices

• 3.3 Social Practices in Deliberation4 Scenario; 5 Discussion and Conclusion; References; Modelling the Impact of Role Specialisation on Cooperative Behaviour in Historic Trader Scenarios; 1 Introduction; 2 Dynamic Deontics; 2.1 Characteristics; 2.2 Operationalisation; 3 Historic Trader Scenarios; 4 Model; 5 Simulation Results; 5.1 Role Unification Without Norm Enforcement; 5.2 Role Unification with Norm Enforcement; 5.3 Role Specialisation Without Norm Enforcement; 5.4 Role Specialisation with Norm Enforcement; 6 Discussion, Conclusions and Outlook; References

• Severity-Sensitive Robustness Analysis in Normative Systems1 Introduction; 2 ISR Scenario; 3 Formalization; 4 Normative Ranking of Possible Worlds; 4.1 Computing Pw; 4.2 Computing the Ranking; 5 Detailed Example; 6 Checking Robustness; 7 Discussion; 8 Conclusions; Building an Artificial Primitive Human Society: An Agent-Based Approach; Abstract; 1 Introduction; 2 Background; 2.1 Simulation Environment and Settings; 3 Experimental Settings and Results; 4 Discussion; 5 Conclusion and Future Direction; References; Designing for Planned Emergence in Multi-agent Systems; 1 Introduction

• 2 OJAzzIC Overview3 Design Considerations; 3.1 Scenario; 3.2 Design Questions; 3.3 Designing an OJAzzIC Based System; 4 Incident Response Demonstration System; 4.1 Define the Goal Model; 4.2 Define the Organizational Model; 4.3 Define the Agent Capabilities Model; 4.4 Define the Role Model; 4.5 Establish Social Policies; 5 Observations; 6 Related Work; 7 Conclusion; References; Supporting Request Acceptance with Use Policies; 1 Introduction; 2 Related Work; 3 Overview; 4 Requests and Use Policies; 4.1 Representations; 4.2 Conflict; 5 The Event Calculus Normative Model; 5.1 Event Calculus

This book constitutes the thoroughly refereed post-conference proceedings of the 10th International Workshops on Coordination, Organizations, Institutions and Norms in Agent Systems, COIN 2014. The workshops were co-located with AAMAS 2014, held in Paris, France, in May 2014, and with PRICAI 2014, held in Gold Coast, QLD, Australia, in December 2014. The 16 full papers were carefully reviewed and selected for inclusion in this volume. The papers cover a wide range of topics from work on formal aspects of normative and team based systems, to software engineering with organisational concepts, to applications of COIN based systems, and to philosophical issues surrounding socio-technical systems. They highlight not only the richness of existing work in the field, but also point out the challenges and exciting research that remains to be done in the area.
(source: Nielsen Book Data)

• 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)

• 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)

• 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)

• 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)

• 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.

• 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)

• 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.

• 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)

• 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)

• 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.

• 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)

• 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)

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)

• 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)

• 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)

• ""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)

• 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)

• Related Work.- Periodic Patterns.- Statistically Significant Patterns.- Approximate Patterns.- Conclusion Remark.
• (source: Nielsen Book Data)

In many applications, e.g., bioinformatics, web access traces, system u- lization logs, etc., the data is naturally in the form of sequences. It has been of great interests to analyze the sequential data to find their inherent char- teristics. The sequential pattern is one of the most widely studied models to capture such characteristics. Examples of sequential patterns include but are not limited to protein sequence motifs and web page navigation traces. In this book, we focus on sequential pattern mining. To meet different needs of various applications, several models of sequential patterns have been proposed. We do not only study the mathematical definitions and application domains of these models, but also the algorithms on how to effectively and efficiently find these patterns. The objective of this book is to provide computer scientists and domain - perts such as life scientists with a set of tools in analyzing and understanding the nature of various sequences by : (1) identifying the specific model(s) of - quential patterns that are most suitable, and (2) providing an efficient algorithm for mining these patterns. Chapter 1 INTRODUCTION Data Mining is the process of extracting implicit knowledge and discovery of interesting characteristics and patterns that are not explicitly represented in the databases. The techniques can play an important role in understanding data and in capturing intrinsic relationships among data instances. Data mining has been an active research area in the past decade and has been proved to be very useful.
(source: Nielsen Book Data)
The focus of this book is sequential pattern mining. In many applications, such as bioinformatics, web access traces, system utilization logs, etc., the data is naturally in the form of sequences. This information has been of great interest for analyzing the sequential data to find its inherent characteristics. Examples of sequential patterns include but are not limited to protein sequence motifs and web page navigation traces.
(source: Nielsen Book Data)

• I. Introduction
• 1. Nudge Nudge Wink Wink: Elements of Face-to-Face Conversation for Embodied Conversation Agents / Justine Cassell
• II. Systems
• 2. Human Conversation as a System Framework: Designing Embodied Conversational Agents / Justine Cassell, Tim Bickmore and Lee Campbell / [and others]
• 3. "May I Help You?": Designing Embodied Conversational Agent Allies / Elizabeth F. Churchill, Linda Cook and Peter Hodgson / [and others]
• 4. Task-Oriented Collaboration with Embodied Agents in Virtual Worlds / Jeff Rickel and W. Lewis Johnson
• 5. Deictic and Emotive Communication in Animated Pedagogical Agents / James C. Lester, Stuart G. Towns and Charles B. Callaway / [and others]
• 6. Performative Facial Expressions in Animated Faces / Isabella Poggi and Catherine Pelachaud
• 7. Emotion and Personality in a Conversational Agent / Gene Ball and Jack Breese
• 8. The Automated Design of Believable Dialogues for Animated Presentation Teams / Elisabeth Andre, Thomas Rist and Susanne van Mulken / [and others]
• 9. Parameterized Action Representation for Virtual Human Agents / Norman I. Badler, Rama Bindiganavale and Jan Allbeck / [and others]
• III. Evaluation
• 10. Developing and Evaluating Conversational Agents / Dominic W. Massaro, Michael M. Cohen and Jonas Beskow / [and others]
• 11. Designing and Evaluating Conversational Interfaces with Animated Characters / Sharon Oviatt and Bridget Adams
• 12. Measurement and Evaluation of Embodied Conversational Agents / Greogry A. Sanders and Jean Scholtz
• 13. Truth Is Beauty: Researching Embodied Conversational Agents / Clifford Nass, Katherine Isbister and Eun-Ju Lee.

This book describes research in all aspects of the design, implementation, and evaluation of embodied conversational agents as well as details of specific working systems. Embodied conversational agents are computer-generated cartoonlike characters that demonstrate many of the same properties as humans in face-to-face conversation, including the ability to produce and respond to verbal and nonverbal communication. They constitute a type of (a) multimodal interface where the modalities are those natural to human conversation: speech, facial displays, hand gestures, and body stance; (b) software agent, insofar as they represent the computer in an interaction with a human or represent their human users in a computational environment (as avatars, for example); and (c) dialogue system where both verbal and nonverbal devices advance and regulate the dialogue between the user and the computer. With an embodied conversational agent, the visual dimension of interacting with an animated character on a screen plays an intrinsic role. Not just pretty pictures, the graphics display visual features of conversation in the same way that the face and hands do in face-to-face conversation among humans. This book describes research in all aspects of the design, implementation, and evaluation of embodied conversational agents as well as details of specific working systems. Many of the chapters are written by multidisciplinary teams of psychologists, linguists, computer scientists, artists, and researchers in interface design. The authors include Elisabeth Andre, Norm Badler, Gene Ball, Justine Cassell, Elizabeth Churchill, James Lester, Dominic Massaro, Cliff Nass, Sharon Oviatt, Isabella Poggi, Jeff Rickel, and Greg Sanders.
(source: Nielsen Book Data)

Both specialists and laymen will enjoy reading this book. Using a lively, non-technical style and images from everyday life, the authors present the basic principles behind computing and computers. The focus is on those aspects of computation that concern networks of numerous small computational units, whether biological neural networks or artificial electronic devices.
(source: Nielsen Book Data)

• Chapter 1. Designing a method of data transfer using dual message queue brokers in an IoT environment (Hyun-Seong Lee).-
• Chapter 2. Bluetooth Low Energy Plate and PDR Hybrid for Indoor Navigation (Hee-Yong Kang).-
• Chapter 3. A Study on the Common Collaboration Platform Activation of Cloud-based Manufacturing Supply Management System (SCM) (Sanhae Kim).-
• Chapter 4. Service Management and Model Driven Management (Haeng-Kon Kim).-
• Chapter 5. Measuring the Effectiveness of E-Wallet in Malaysia (Faisal Nizam).-
• Chapter 6. Designing of Domain Modeling for Mobile Applications Development (Haeng-Kon Kim) etc.
• (source: Nielsen Book Data)

This book presents the outcomes of the 3rd IEEE/ACIS International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD 2018), which was held on July 10-12, 2018 in Kanazawa. The aim of the conference was to bring together researchers and scientists, businesspeople and entrepreneurs, teachers, engineers, computer users, and students to discuss the various fields of computer science, to share their experiences, and to exchange new ideas and information in a meaningful way. All aspects (theory, applications and tools) of computer and information science, the practical challenges encountered along the way, and the solutions adopted to solve them are all explored here. The conference organizers selected the best papers from among those accepted for presentation. The papers were chosen on the basis of review scores submitted by members of the program committee and subsequently underwent further rigorous review. Following this second round of review, 13 of the conference's most promising papers were selected for this Springer (SCI) book. We eagerly await the important contributions that we know these authors will make to the field of computer and information science.
(source: Nielsen Book Data)

• Optimal Group Decision Making Can Help Decrease Poverty, Inequality, and Discrimination.- An Overview and Re-Interpretation of Paradoxes of Responsiveness.- Veto in Yes-no and Yes-no-abstain Voting Systems.- Power Indices for Finance.- The binomial decomposition of the single parameter family of GB welfare functions.- The logic of information and processes in the system-of-systems applications.- Decision-making Process using Hyperstructures and Fuzzy Structures in the Social Sciences.- Social Preferences through Riesz Spaces: A First Approach.- Coherent conditional plausibility: a tool for handling fuzziness and uncertainty under partial information.- Intuitionistic fuzzy interpretations of some formulas for estimation of preference degree.- Fuzzified Likert Scales in Group Multiple-criteria Evaluation.- Maximal Entropy and Minimal Variability OWA Operator Weights: A Short Survey of Recent Developments.
• (source: Nielsen Book Data)

This book offers a concise introduction and comprehensive overview of the state of the art in the field of decision-making and consensus modeling, with a special emphasis on fuzzy methods. It consists of a collection of authoritative contributions reporting on the decision-making process from different perspectives: from psychology to social and political sciences, from decision sciences to data mining, and from computational sciences in general, to artificial and computational intelligence and systems. Written as a homage to Mario Fedrizzi for his scholarly achievements, creative ideas and long lasting services to different scientific communities, it introduces key theoretical concepts, describes new models and methods, and discusses a range of promising real-world applications in the field of decision-making science. It is a timely reference guide and a source of inspiration for advanced students and researchers.
(source: Nielsen Book Data)

• BiTrust: A Comprehensive Trust Management Model for Multi-agent Systems.- Using Reference Points for Competitive Negotiations in Service Composition.- A Cooperative Framework for Mediated Group Decision Making.- A CPN-Based Protocol for Concurrent Multiple Negotiation
• A Dependency-based Mediation Mechanism for Complex Negotiations.- Using graph properties and clustering techniques to select division mechanisms for scalable negotiations.- Compromising Strategy considering Interdependencies of Issues for Multi-issue Closed Nonlinear Negotiations.
• (source: Nielsen Book Data)

This book addresses several important aspects of complex automated negotiations and introduces a number of modern approaches for facilitating agents to conduct complex negotiations. It demonstrates that autonomous negotiation is one of the most important areas in the field of autonomous agents and multi-agent systems. Further, it presents complex automated negotiation scenarios that involve negotiation encounters that may have, for instance, a large number of agents, a large number of issues with strong interdependencies and/or real-time constraints.
(source: Nielsen Book Data)

• Part I Fuzzy Logic Theory
• Part II Neural Networks Theory
• Part III Neural Networks Applications
• Part IV Nature Inspired Optimization
• Part V Nature Inspired Optimization Applications
• Part VI Optimization: Theory and Applications
• Part VII Fuzzy Logic Applications
• Part VIII Fuzzy Logic and Metaheuristic.

This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in eight main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on fuzzy systems. The second part contains papers with the main theme of neural networks theory, which are basically papers dealing with new concepts and algorithms in neural networks. The third part contains papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The fourth part contains papers describing new nature-inspired optimization algorithms. The fifth part presents diverse applications of nature-inspired optimization algorithms. The sixth part contains papers describing new optimization algorithms. The seventh part contains papers describing applications of fuzzy logic in diverse areas, such as time series prediction and pattern recognition. Finally, the eighth part contains papers that present enhancements to meta-heuristics based on fuzzy logic techniques.

• On Reasoning With Words and Perceptions
• Language, Fuzzy Logic, Metalogic
• On What I Still Hope from Fuzzy Logic
• Fuzzy Logic and Modern Economics
• Linguistic Summaries of Time Series: A Powerful and Prospective Tool for Discovering Knowledge on Time Varying Processes And Systems.-Granular Geometry
• Inquiry About the Origin And Abundance of Vague Language: an Issue for the Future
• Fuzzy Natural Logic: Towards Mathematical Logic of Human Reasoning
• From Lattice Valued Theories to Lattice Valued Analysis
• Applying Fuzzy Mathematics to Empirical Work inPolitical Science
• Crisis' Origin's Causes. Contributions from the Fuzzy Logic in the Sustainability on the Socio-economic Systems
• Advanced Computing with Words: Status and Challenges
• Informal Meditation on Empiricism and Approximation in Fuzzy Logic and Set Theory: Descriptive Normativity, Formal Informality and Objective Subjectivity
• Formalizing the Informal, Precisiating the Imprecise: How Fuzzy Logic Can Help Mathematicians and Physicists by Formalizing their Intuitive Ideas
• Future is where Concepts, Theories and Applications Meet (Also in Fuzzy Logic)
• Graduated Conjectures
• Fuzzy Concepts and Fuzzy Logic in Historical and Genetic Epistemology.

This book provides readers with a snapshot of the state-of-the art in fuzzy logic. Throughout the chapters, key theories developed in the last fifty years as well as important applications to practical problems are presented and discussed from different perspectives, as the authors hail from different disciplines and therefore use fuzzy logic for different purposes. The book aims at showing how fuzzy logic has evolved since the first theory formulation by Lotfi A. Zadeh in his seminal paper on Fuzzy Sets in 1965. Fuzzy theories and implementation grew at an impressive speed and achieved significant results, especially on the applicative side. The study of fuzzy logic and its practice spread all over the world, from Europe to Asia, America and Oceania. The editors believe that, thanks to the drive of young researchers, fuzzy logic will be able to face the challenging goals posed by computing with words. New frontiers of knowledge are waiting to be explored. In order to motivate young people to engage in the future development of fuzzy logic, fuzzy methodologies, fuzzy applications, etc., the editors invited a team of internationally respected experts to write the present collection of papers, which shows the present and future potentials of fuzzy logic from different disciplinary perspectives and personal standpoints.

• Machine generated contents note: Nursing-Task Recognition Using Accelerometers and RFID / Henrik Christensen / Emil Petriu / Frans Groen
• Recognizing Nonverbal Affective Behavior in Humanoid Robots / Lounell Bahoy Gueta / Jun Ota / Yasuko Kitajima / Miwa Hirata / Jukai Maeda / Noriaki Kuwahara / Masako Kanai-Pak / Yoshihiro Takebe
• Feasibility of Interactive Localization and Navigation of People with Visual Impairments / Ronald C. Arkin / Lilia Moshkina / Sunghyun Park
• Sequentially Greedy Unsupervised Learning of Gaussian Mixture Models by Means of a Binary Tree Structure / Navid Fallah / Kostas E. Bekris / Eelke Folmer / Ilias Apostolopoulos
• On Ambiguity in Robot Learning from Demonstration / Nicola Greggio / José Santos-Victor / Alexandre Bernardino
• Recognition and Motion Primitives for Autonomous Clothes Unfolding of Humanoid Robot / Thomas Hellström / Suna Bensch -- ^ 3D Sensing System for Tracked Vehicle Robot Using Movable Laser Range Finder / Youhei Kakiuchi / Kei Okada / Masayuki Inaba / Hiroko Kobori
• Simulating Robotic Auditory Environments / Toyomi Fujita / Yuya Kondo
• Development of a Person Following Robot and Its Experimental Evaluation / Eric Martinson
• Evaluating Pose Estimation Methods for Stereo Visual Odometry on Robots / Jun Miura / Junji Satake / Masaya Chiba / Yuki Ishikawa / Kenta Kitajima / Hiroaki Masuzawa
• Object Search on a Mobile Robot Using Relational Spatial Information / Hatem Alismail / Brett Browning / M. Bernardine Dias
• Safe Navigation on a Mobile Robot Using Local and Temporal Visual Cues / Alper Aydemir / Kristoffer Sjöö / Patric Jensfelt
• Representing Spatial Knowledge in Mobile Cognitive Systems / Xiang Li / Mohan Sridharan
• Landmark Rating and Selection Considering the Observability Regions / Andrzej Pronobis / Kristoffer Sjö

This volume contains the proceedings of the eleventh International Conference on Intelligent Autonomous Systems (IAS-11) at the University of Ottawa in Canada. As ever, the purpose of the IAS conference is to bring together leading international researchers with an interest in all aspects of the autonomy and adaptivity of artificial systems. This year the conference reflects in particular a current trend: the symbiotic interaction of humans with intelligent systems. Of the 35 papers submitted for this yea's conference, 25 have been accepted for presentation. These papers cover a wide spectrum of research in autonomous intelligent systems including interactive systems, learning, perception, localization and mapping, navigation planning and distributed systms. This proceedings includes all the accepted papers and reflects the wide variety of topics of current concern to all those involved in the use, research and development of intelligent autonomous systems.

• Preface
• 1. The smallest free number
• 2. A surpassing problem
• 3. Improving on saddleback search
• 4. A selection problem
• 5. Sorting pairwise sums
• 6. Making a century
• 7. Building a tree with minimum height
• 8. Unravelling greedy algorithms
• 9. Finding celebrities
• 10. Removing duplicates
• 11. Not the maximum segment sum
• 12. Ranking suffixes
• 13. The Burrows-Wheeler transform
• 14. The last tail
• 15. All the common prefixes
• 16. The Boyer-Moore algorithm
• 17. The Knuth-Morris-Pratt algorithm
• 18. Planning solves the rush hour problem
• 19. A simple Sudoku solver
• 20. The countdown problem
• 21. Hylomorphisms and nexuses
• 22. Three ways of computing determinants
• 23. Inside the convex hull
• 24. Rational arithmetic coding
• 25. Integer arithmetic coding
• 26. The Schorr-Waite algorithm
• 27. Orderly insertion
• 28. Loopless functional algorithms
• 29. The Johnson-Trotter algorithm
• 30. Spider spinning for dummies
• Index.
• (source: Nielsen Book Data)

Richard Bird takes a radical approach to algorithm design, namely, design by calculation. These 30 short chapters each deal with a particular programming problem drawn from sources as diverse as games and puzzles, intriguing combinatorial tasks, and more familiar areas such as data compression and string matching. Each pearl starts with the statement of the problem expressed using the functional programming language Haskell, a powerful yet succinct language for capturing algorithmic ideas clearly and simply. The novel aspect of the book is that each solution is calculated from an initial formulation of the problem in Haskell by appealing to the laws of functional programming. Pearls of Functional Algorithm Design will appeal to the aspiring functional programmer, students and teachers interested in the principles of algorithm design, and anyone seeking to master the techniques of reasoning about programs in an equational style.
(source: Nielsen Book Data)

• Cover; Copyright; Credits; Foreword; The Kettle Project; About the Author; About the Reviewers; Table of Contents; Preface; Chapter 1: Getting started with Pentaho Data Integration; Pentaho Data Integration and Pentaho BI Suite; Exploring the Pentaho Demo; Pentaho Data Integration; Using PDI in real world scenarios; Loading data warehouses or data marts; Integrating data; Data cleansing; Migrating information; Exporting data; Integrating PDI using Pentaho BI; Installing PDI; Time for action
• installing PDI; Launching the PDI graphical designer: Spoon

• Time for action
• starting and customizing SpoonSpoon; Setting preferences in the Options window; Storing transformations and jobs in a repository; Creating your first transformation; Time for action
• creating a hello world transformation; Directing the Kettle engine with transformations; Exploring the Spoon interface; Running and previewing the transformation; Time for action
• running and previewing the hello_world; transformation; Installing MySQL; Time for action
• installing MySQL on Windows; Time for action
• installing MySQL on Ubuntu; Summary

• Chapter 2: Getting Started with TransformationsReading data from files; Time for action
• reading results of football matches from files; Input files; Input steps; Reading several files at once; Time for action
• reading all your files at a time using a single; Text file input step; Time for action
• reading all your files at a time using a single; Text file input step and regular expressions; Regular expressions; Grids; Sending data to files; Time for action
• sending the results of matches to a plain file; Output files; Output steps; Some data definitions; Rowset; Streams

• The Select values stepGetting system information; Time for action
• updating a file with news about examinations; Getting information by using Get System Info step; Data types; Date fields; Numeric fields; Running transformations from a terminal window; Time for action
• running the examination transformation from; a terminal window; XML files; Time for action
• getting data from an XML file with information; about countries; What is XML; PDI transformation files; Getting data from XML files; XPath; Configuring the Get data from XML step; Kettle variables; How and when you can use variables

• Time for action
• finding out which language people speak

As part of Packt's Beginner's Guide, this book focuses on teaching by example. The book walks you through every aspect of PDI, giving step-by-step instructions in a friendly style, allowing you to learn in front of your computer, playing with the tool. The extensive use of drawings and screenshots make the process of learning PDI easy. Throughout the book numerous tips and helpful hints are provided that you will not find anywhere else. The book provides short, practical examples and also builds from scratch a small datamart intended to reinforce the learned concepts and to teach you the basics of data warehousing. This book is for software developers, database administrators, IT students, and everyone involved or interested in developing ETL solutions, or, more generally, doing any kind of data manipulation. If you have never used PDI before, this will be a perfect book to start with. You will find this book is a good starting point if you are a database administrator, data warehouse designer, architect, or any person who is responsible for data warehouse projects and need to load data into them. You don't need to have any prior data warehouse or database experience to read this book. Fundamental database and data warehouse technical terms and concepts are explained in easy-to-understand language.
(source: Nielsen Book Data)

• Agents and Multiagent Systems.- A Security Proposal Based on a Real Time Agent to Protect Web Services Against DoS Attack.- Approaching Real-Time Intrusion Detection through MOVICAB-IDS.- Hybrid Dynamic Planning Mechanism for Virtual Organizations.- Combinatorial Auctions for Coordination and Control of Manufacturing MAS: Updating Prices Methods.- Intelligent Systems.- A Software Tool for Harmonic Distortion Simulation Caused by Non-linear Household Loads.- A Multiobjective Variable Neighborhood Search for Solving the Motif Discovery Problem.- Solving the Routing and Wavelength Assignment Problem in WDM Networks by Using a Multiobjective Variable Neighborhood Search Algorithm.- iGenda: An Event Scheduler for Common Users and Centralised Systems.- Scalable Intelligence and Adaptation in Scheduling DSS.- Evolutionary Computing.- A Parallel Cooperative Evolutionary Strategy for Solving the Reporting Cells Problem.- Optimization of Parallel Manipulators Using Evolutionary Algorithms.- Multi-criteria Manipulator Trajectory Optimization Based on Evolutionary Algorithms.- Combining Heuristics Backtracking and Genetic Algorithm to Solve the Container Loading Problem with Weight Distribution.- A Decision Support System for Logistics Operations.- Energy and Environmental Applications.- Greenhouse Heat Load Prediction Using a Support Vector Regression Model.- Evaluating the Low Quality Measurements in Lighting Control Systems.- Soft Computing Models for an Environmental Application.- Hybrid Systems.- GRASP Algorithm for Optimization of Grids for Multiple Classifier System.- A Scatter Search Based Approach to Solve the Reporting Cells Problem.- Fuzzy Optimization of Start-Up Operations for Combined Cycle Power Plants.- Catalog Segmentation by Implementing Fuzzy Clustering and Mathematical Programming Model.- Multi-Network-Feedback-Error-Learning with Automatic Insertion.- Applications.- An Optimized 3D Surface Reconstruction Method Using Spatial Kalman Filtering of Projected Line Patterns.- Decision Making and Quality-of-Information.- The Gene Expression Programming Applied to Demand Forecast.- Brain Magnetic Resonance Spectroscopy Classifiers.- A Bio-inspired Ensemble Model for Food Industry Applications.- Hybrid Intelligent Systems and Applications.- Implementation of a New Hybrid Methodology for Fault Signal Classification Using Short -Time Fourier Transform and Support Vector Machines.- Advances in Clustering Search.- WSAN QoS Driven Control Model for Building Operations.- Intelligent Hybrid Control Model for Lighting Systems Using Constraint-Based Optimisation.
• (source: Nielsen Book Data)

This volume of Advances in Intelligent and Soft Computing contains accepted - pers presented at SOCO 2010 held in the beautiful and historic city of Guimaraes, Portugal, June 2010. The global purpose of SOCO conferences has been to provide a broad and - terdisciplinary forum for soft computing and associated paradigms, which are playing increasingly important roles in an important number of industrial and - vironmental applications fields. Soft computing represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines, which - vestigate, simulate and analyze very complex issues and phenomena. This wo- shop is mainly focused on its industrial and environmental applications. th SOCO 2010 is the 5 International Workshop on Soft Computing Models in Industrial Applications and provides interesting opportunities to present and d- cuss the latest theoretical advances and real world applications in this multidis- plinary research field. This volume presents the papers accepted for the 2010 edition, both for the main event and the Special Sessions. SOCO 2010 Special Sessions are a very u- ful tool in order to complement the regular program with new or emerging topics of particular interest to the participating community. Special Sessions that emp- size on multi-disciplinary and transversal aspects, as well as cutting-edge topics were especially encouraged and welcome. SOCO 2010 included a total of 3 Special Sessions: Ensemble Learning and - formation Fusion for Industrial Applications; Soft Computing for Service M- agement; Hybrid Intelligent Systems and Applications.
(source: Nielsen Book Data)

• Introduction
• State of the art
• MORPHEUS architecture overview
• Flexeos embedded FPGA solution
• The dream digital signal processor
• XPP-III
• The hardware services
• The MORPHEUS data communication and storage infrastructure
• Overall MORPHEUS toolset flow
• The Molen organisation and programming paradigm
• Control of dynamic reconfiguration
• Specification tools for spatial design
• Spatial design
• Real-time digital film processing
• Ethernet based in-service reconfiguration of socs in telecommunication networks
• Homeland security
• image processing for intelligent cameras
• PHY-layer of 802.16 mobile wireless on a hardware accelerated SoC
• Conclusions
• Training
• Dissemination of MORPHEUS results
• Exploitation from the MORPHEUS project
• Project management.

Dynamic System Reconfiguration in Heterogeneous Platformsdefines the MORPHEUSplatform that can join the performance density advantage of reconfigurable technologies and the easy control capabilities of general purpose processors. It consists of a System-on-Chip made of a scalable system infrastructure hosting heterogeneous reconfigurable accelerators, providing dynamic reconfiguration capabilities and data-stream management capabilities. Moreover a toolset which offers a software-oriented approach for implementing data intensive applications on the chip is presented. The toolset provides application design process based on high level programming languages as much as possible. Various applications from differnt domains have been selected to drive the development of the project in assessing the MORPHEUS concept about its computing performance, utilization flexibility and productivity. The emerging IEEE 802.16j standard for Mobile Broadband Wireless Access systems is the base for a first type of applications. The second application is in the area of telecommunication networks which requires data rates up to 40 Gbit/s per single line. A third application is about huge digital postprocessing of the films captured by digital camera or film scanners in resolutions up to 4K. The fourth application is about image processing for intelligent cameras.

• Evolutionary Computation: from Genetic Algorithms to Genetic Programming.- Automatically Defined Functions in Gene Expression Programming.- Evolving Intrusion Detection Systems.- Evolutionary Pattern Matching Using Genetic Programming.- Genetic Programming in Data Modelling.- Stock Market Modeling Using Genetic Programming Ensembles.- Evolutionary Digital Circuit Design Using Genetic Programming.- Evolving Complex Robotic Behaviors Using Genetic Programming.- Automatic Synthesis of Microcontroller Assembly Code Through Linear Genetic Programming.
• (source: Nielsen Book Data)

Designing complex programs such as operating systems, compilers, filing systems, data base systems, etc. is an old ever lasting research area. Genetic programming is a relatively new promising and growing research area. Among other uses, it provides efficient tools to deal with hard problems by evolving creative and competitive solutions. Systems Programming is generally strewn with such hard problems. This book is devoted to reporting innovative and significant progress about the contribution of genetic programming in systems programming. The contributions of this book clearly demonstrate that genetic programming is very effective in solving hard and yet-open problems in systems programming. Followed by an introductory chapter, in the remaining contributed chapters, the reader can easily learn about systems where genetic programming can be applied successfully. These include but are not limited to, information security systems, compilers, data mining systems, stock market prediction systems, robots and automatic programming.
(source: Nielsen Book Data)

• Ch. 1. Complex-valued neural networks: an introduction / Akira Hirose
• ch. 2. Orthogonal decision boundaries and generalization of complex-valued neural networks / Tohru Nitta
• ch. 3. Complex-valued neural associative memories: network stability and learning algorithm / Donq-Liang Lee
• ch. 4. A model of complex-valued associative memories and its dynamics / Yasuaki Kuroe
• ch. 5. Clifford networks / Justin Pearson
• ch. 6. Complex associative memory and complex single neuron model / Iku Nemoto
• ch. 7. Data-reusing algorithm for complex-valued adaptive filters / Damlo P. Mandic, Su Lee Goh and Andrew Hanna
• ch. 8. Instantaneously trained neural networks with complex inputs / Pritam Rajagopal and Subhash Kak
• ch. 9. Applications of complex-valued neural networks for image processing / Hiroyuki Aoki
• ch. 10. Memorization of melodies using complex-valued recurrent neural network / Makoto Kinouchi and Masafumi Hagiwara
• ch. 11. Complex-valued generalized Hebbian algorithm and its applications to sensor array signal processing / Yanwu Zhang
• ch. 12. Phasor model with application to multiuser communication / Teruyuki Miyajima and Kazuo Yamanaka
• ch. 13. Adaptive interferometric radar image processing by using complex-valued neural network / Andriyan Bayu Suksmono and Akira Hirose
• ch. 14. Complex neural network model with analogy to self-oscillation generated in an optical phase-conjugate resonator / Mitsuo Takeda and Takaaki Kishigami
• ch. 15. Coherent lightwave neural network systems: use of frequency domain / Sotaro Kawata and Akira Hirose.

In recent years, complex-valued neural networks have widened the scope of application in optoelectronics, imaging, remote sensing, quantum neural devices and systems, spatiotemporal analysis of physiological neural systems, and artificial neural information processing. In this first-ever book on complex-valued neural networks, the most active scientists at the forefront of the field describe theories and applications from various points of view to provide academic and industrial researchers with a comprehensive understanding of the fundamentals, features and prospects of the powerful complex-valued networks.

• Introduction to agent engineering, J.-M. Liu et al
• why autonomy makes the agent, S. Joseph and T. Kawamura
• knowledge granularity spectrum, action pyramid and the scaling problem, Y.-M. Ye and J.K. Tsotsos
• the motivation for dynamic decision-making frameworks in multi-agent systems, K.S. Barber and C.E. Martin
• dynamically organizing KDD processes in a multi-agent KDD system, N. Zhong et al
• self-organized intelligence, J.-M. Liu
• valuation-based coalition formation in multi-agent systems, S.J. Johansson
• simulating how to cooperate in iterated chicken and prisoner's dilemma games, B. Carlsson
• training intelligent agents using human data collected on the Internet, E. Sklar et al
• agent dynamics - soap paradigm, F.W.K. Lor.
• (source: Nielsen Book Data)

Agent engineering concerns the development of autonomous computational or physical entities capable of perceiving, reasoning, adapting, learning, cooperating and delegating in a dynamic environment. It is one of the most promising areas of research and development in information technology, computer science and engineering.This book addresses some of the key issues in agent engineering: What is meant by "autonomous agents"? How can we build agents with autonomy? What are the desirable capabilities of agents with respect to surviving (they will not die) and living (they will furthermore enjoy their being or existence)? How can agents cooperate among themselves? In order to achieve the optimal performance at the global level, how much optimization at the local, individual level and how much at the global level would be necessary?
(source: Nielsen Book Data)

• Albertini and Pra, Recurrent Neural Networks: Identification and Other System Theoretic Properties. Anderson and Titterington, Boltzmann Machines: Statistical Associations and Algorithms for Training Anderson and Titterington. Campbell, Constructive Learning Techniques for Designing Neural Network Systems. Mehrotra and Mohan, Modular Neural Networks. Xu and Kwong, Associative Memories. Fry and Sova, A Logical Basis for Neural Network Design.Hoekstra, Duin, and Kraaijveld, Neural Networks Applied to Data Analysis. Zhang and Wang, Multi-Mode Single Neuron Arithmetics.
• (source: Nielsen Book Data)

Optimization Techniques is a unique reference source to a diverse array of methods for achieving optimization, and includes both systems structures and computational methods. The text devotes broad coverage toa unified view of optimal learning, orthogonal transformation techniques, sequential constructive techniques, fast back propagation algorithms, techniques for neural networks with nonstationary or dynamic outputs, applications to constraint satisfaction, optimization issues and techniques for unsupervised learning neural networks, optimum Cerebellar Model of Articulation Controller systems, a new statistical theory of optimum neural learning, and the role of the Radial Basis Function in nonlinear dynamical systems.This volume is useful for practitioners, researchers, and students in industrial, manufacturing, mechanical, electrical, and computer engineering.
(source: Nielsen Book Data)

• Fuzzy compensation principles
• normal fuzzy reasoning methodology
• compensatory genetic fuzzy neural networks
• fuzzy knowledge rediscovery in fuzzy rule bases
• fuzzy cat-pole balancing control systems
• fuzzy knowledge compression and expansion
• highly nonlinear system modelling and prediction
• fuzzy moves in fuzzy games
• genetic neuro-fuzzy pattern recognition
• constructive approach to modelling fuzzy systems.
• (source: Nielsen Book Data)

This book presents a powerful hybrid intelligent system based on fuzzy logic, neural networks, genetic algorithms and related intelligent techniques. The new compensatory genetic fuzzy neural networks have been widely used in fuzzy control, nonlinear system modeling, compression of a fuzzy rule base, expansion of a sparse fuzzy rule base, fuzzy knowledge discovery, time series prediction, fuzzy games and pattern recognition. This effective soft computing system is able to perform both linguistic-word-level fuzzy reasoning and numerical-data-level information processing. The book also proposes various novel soft computing techniques.
(source: Nielsen Book Data)

Build simple, maintainable, and easy to deploy machine learning applications. About This Book * Build simple, but powerful, machine learning applications that leverage Go's standard library along with popular Go packages. * Learn the statistics, algorithms, and techniques needed to successfully implement machine learning in Go * Understand when and how to integrate certain types of machine learning model in Go applications. Who This Book Is For This book is for Go developers who are familiar with the Go syntax and can develop, build, and run basic Go programs. If you want to explore the field of machine learning and you love Go, then this book is for you! Machine Learning with Go will give readers the practical skills to perform the most common machine learning tasks with Go. Familiarity with some statistics and math topics is necessary. What You Will Learn * Learn about data gathering, organization, parsing, and cleaning. * Explore matrices, linear algebra, statistics, and probability. * See how to evaluate and validate models. * Look at regression, classification, clustering. * Learn about neural networks and deep learning * Utilize times series models and anomaly detection. * Get to grip with techniques for deploying and distributing analyses and models. * Optimize machine learning workflow techniques In Detail The mission of this book is to turn readers into productive, innovative data analysts who leverage Go to build robust and valuable applications. To this end, the book clearly introduces the technical aspects of building predictive models in Go, but it also helps the reader understand how machine learning workflows are being applied in real-world scenarios. Machine Learning with Go shows readers how to be productive in machine learning while also producing applications that maintain a high level of integrity. It also gives readers patterns to overcome challenges that are often encountered when trying to integrate machine learning in an engineering organization. The readers will begin by gaining a solid understanding of how to gather, organize, and parse real-work data from a variety of sources. Readers will then develop a solid statistical toolkit that will allow them to quickly understand gain intuition about the content of a dataset. Finally, the readers will gain hands-on experience implementing essential machine learning techniques (regression, classification, clustering, and so on) with the relevant Go packages. Finally, the reader will have a solid machine learning mindset and a powerful Go toolkit of techniques, packages, and example implementations. Style and approach This book connects the fundamental, theoretical concepts behind Machine Learning to practical implementations using the Go programming language.
(source: Nielsen Book Data)

• Cloud Computing
• Social Networking
• Monitoring
• Literature Management.

This book presents some of the emerging techniques and technologies used to handle Web data management. Authors present novel software architectures and emerging technologies and then validate using experimental data and real world applications. The contents of this book are focused on four popular thematic categories of intelligent Web data management: cloud computing, social networking, monitoring and literature management. The Volume will be a valuable reference to researchers, students and practitioners in the field of Web data management, cloud computing, social networks using advanced intelligence tools.

Annotation Intelligent Technologies including neural network, evolutionary computations, fuzzy approach and mainly hybrid approaches are very promising tools to build intelligent technologies in general. The progress of each theory or application is provided by a number of various theoretical as well as applicational experiments. Machine intelligence is the only alternative how to increase the level of technology to make technology more human-centred and more effective for society. This book includes theoretical as well as applicational papers in the field of neural networks, fuzzy systems and mainly evolutionary computations which application potential was increased by enormous progress in computer power. Hybrid technologies are still progressing and are trying to make some more applications with their ability to learn and process fuzzy information. Neurogenetic systems are very interesting approach to make systems re-configurable and on-line systems for real-world applications. The book is presenting papers from Japan, USA, Hungary, Poland, Germany, Finland, France, Slovakia, United Kingdom, Czech Republic and some other countries. This publication provides the latest state of the art in the field and could be contributed to theory and applications in the machine intelligence tools and their wide application potential in current and future technologies within the Information Society.

• Helicopter flight control with fuzzy logic and genetic algorithms, C. Philips et al
• skill acquisition and skill-based motion planning for hierarchical intelligent control of a redundant manipulator, T. Shibata
• a creative design of fuzzy logic controller using a genetic algorithm, T. Hashiyama et al
• automatic fuzzy tuning and its applications, H. Ishigami et al
• an evolutionary algorithm for fuzzy controller synthesis and optimization based on SGS-Thomson's W.A.R.P. fuzzy processor, R. Poluzzi et al
• on-line self-structuring fuzzy inference systems for function approximation, H. Bersini
• fuzzy classification based on adaptive networks and genetic algorithms, C.-T. Sun and J.-S. Jang
• intelligent systems for fraud detection, J. Kingdon
• genetic algorithms for query optimization in information retrieval - relevance feedback, D.H. Kraft et al
• fuzzy fitness assignment in an interactive genetic algorithm for a cartoon face search, K. Nishio et al
• an evolutionary approach to simulate cognitive feedback learning in medical domain, H.S. Lopes et al
• a classified review on the combination fuzzy logic-genetic algorithms bibliography - 1989-1995, O. Cordon et al.
• (source: Nielsen Book Data)

Ever since fuzzy logic was introduced by Lotfi Zadeh in the mid-sixties and genetic algorithms by John Holland in the early seventies, these two fields widely been subjects of academic research the world over. During the last few years, they have been experiencing extremely rapid growth in the industrial world, where they have been shown to be very effective in solving real-world problems. These two substantial fields, together with neurocomputing techniques, are recognized as major parts of soft computing: a set of computing technologies already riding the waves of the next century to produce the human-centered intelligent systems of tomorrow; the collection of papers presented in this book shows the way. The book also contains an extensive bibliography on fuzzy logic and genetic algorithms.
(source: Nielsen Book Data)

• The Road to Intelligent Machines
• The Power and Limitations of Perceptrons
• Neural Logic Networks
• Probabilistic Neural Logic Networks
• Fuzzy Neural Logic Networks
• Temporal Neural Logic Networks
• Neural Logic Programming
• Connectionist Expert Systems
• Fuzzy Knowledge Processing
• The Art of Guessing.
• (source: Nielsen Book Data)

This book is the first of a series of technical reports of a key research project of the Real-World Computing Program supported by the MITI of Japan.The main goal of the project is to model human intelligence by a special class of mathematical systems called neural logic networks.The book consists of three parts. Part 1 describes the general theory of neural logic networks and their potential applications. Part 2 discusses a new logic called Neural Logic which attempts to emulate more closely the logical thinking process of human. Part 3 studies the special features of neural logic networks which resemble the human intuition process.This book should appeal to researchers in artificial intelligence, neural computings and logic, as well as graduate and advance undergraduate students in computer science.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 1st International Congress on Blockchain and Applications 2020, BLOCKCHAIN'20, held in L'Aquila, Italy, in October 2020. Among the scientific community, blockchain and artificial intelligence are a promising combination that will transform the production and manufacturing industry, media, finance, insurance, e-government, etc. Nevertheless, there is no consensus with schemes or best practices that would specify how blockchain and artificial intelligence should be used together. The 21 full papers presented were carefully reviewed and selected from over 40 submissions. They contain the latest advances on blockchain and artificial intelligence and on their application domains, exploring innovative ideas, guidelines, theories, models, technologies, and tools, and identifying critical issues and challenges that researchers and practitioners must deal with in future research. .
(source: Nielsen Book Data)

• Multiagent Systems.- Distributed Artificial Intelligence.- Intelligent Agents.- Agent Architectures.- Multiagent Planning.- Scheduling.- Autonomous Coordination.- Machine Learning.- Agent-based Manufacturing.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 15th German Conference on Multiagent System Technologies, MATES 2017, held in Lepzig, Germany, in August 2017. The 17 full papers presented in this volume were carefully reviewed and selected from 24 submissions for inclusion in the proceedings. Over these 15 years, the MATES conference series has been aiming at the promotion of and the cross-fertilization between theory and application of intelligent agents and multi-agent systems.
(source: Nielsen Book Data)