Artificial intelligence (AI) Books

Book SynopsisKnowledge Engineering (KE) is a field within artificial intelligence that develops knowledgebased systems. KE is the process of imitating how a human expert in a specific domain would act and take decisions. It contains large amounts of knowledge, like metadata and information about a data object that describes characteristics such as content, quality, and format, structure and processes. Such systems are computer programs that are the basis of how a decision is made or a conclusion is reached. It is having all the rules and reasoning mechanisms to provide solutions to real-world problems. This book presents an extensive collection of the recent findings and innovative research in the information system and KE domain. Highlighting the challenges and difficulties in implementing these approaches, this book is a critical reference source for academicians, professionals, engineers, technology designers, analysts, undergraduate and postgraduate students in computing science and related disciplines such as Information systems, Knowledge Engineering, Intelligent Systems, Artifi cial Intelligence, Cognitive Neuro - science, and Robotics. In addition, anyone who is interested or involved in sophisticated information systems and knowledge engineering developments will find this book a valuable source of ideas and guidance.

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Book SynopsisThis book explores the possible applications of Artificial Intelligence in Virtual environments. These were previously mainly associated with gaming, but have largely extended their area of application, and are nowadays used for promoting collaboration in work environments, for training purposes, for management of anxiety and pain, etc.. The development of Artificial Intelligence has given new dimensions to the research in this field.

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Book SynopsisTypical timelines to go from discovery to impact in the advanced materials sector are between 10 to 30 years. Advances in robotics and artificial intelligence are poised to accelerate the discovery and development of new materials dramatically. This book is a primer for any materials scientist looking to future-proof their careers and get ahead of the disruption that artificial intelligence and robotic automation is just starting to unleash. It is meant to be an overview of how we can use these disruptive technologies to augment and supercharge our abilities to discover new materials that will solve world’s biggest challenges. Written by world leading experts on accelerated materials discovery from academia (UC Berkeley, Caltech, UBC, Cornell, etc.), industry (Toyota Research Institute, Citrine Informatics) and national labs (National Research Council of Canada, Lawrence Berkeley National Labs).

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Book SynopsisArtificial intelligence advancements, machine intelligence innovations, and semantic web developments together make up semantic intelligence technologies. The edited book integrates artifi cial intelligence, machine learning, IoT, blockchain, and natural language processing with semantic web technologies. This book also aims to offer real-life solutions to the pressing issues currently being faced by semantic web technologies.

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Book SynopsisData Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data.This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given.Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering.Trade ReviewFrom the book reviews:“This book is a comprehensive collection of data preprocessing techniques used in data mining. Any readers who practice data mining will find it beneficial … . This book is an excellent guideline in the topic of data preprocessing for data mining. It is suitable for both practitioners and researchers who would like to use datasets in their data mining projects.” (Xiannong Meng, Computing Reviews, December, 2014)Table of ContentsIntroduction.- Data Sets and Proper Statistical Analysis of Data Mining Techniques.- Data Preparation Basic Models.- Dealing with Missing Values.- Dealing with Noisy Data.- Data Reduction.- Feature Selection.- Instance Selection.- Discretization.- A Data Mining Software Package Including Data Preparation and Reduction: KEEL.

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Book SynopsisThis book constitutes the refereed proceedings of the 5th International Conference on Serious Games Development and Applications, SGDA 2014, held in Berlin, Germany, in October 2014. The 14 revised full papers presented together with 4 short papers were carefully reviewed and selected from 31 submissions. The focus of the papers was on the following: games for health, games for medical training, serious games for children, music and sound effects, games for other purposes, and game design and theories.Table of ContentsGames for Health.- PhysioVinci – A First Approach on a Physical Rehabilitation Game.- A Pilot Evaluation of a Therapeutic Game Applied to Small Animal Phobia Treatment.- Effect of Ecological Gestures on the Immersion of the Player in a Serious Game.- Using Serious Games for Cognitive Disabilities.- Atmosphaeres – 360◦ Video Environments for Stress and Pain Management.- Games for Medical Training.- Sense: An Interactive Learning Application that Visualizes the Nerve Supply of Face.- Sepsis Fast Track: A Serious Game for Medical Decision Making.- Serious Games for Children.- iBUAT: Paper Prototyping of Interactive Game Design Authoring Tool for Children.- A Review of Serious Games for Children with Autism Spectrum Disorders (ASD).- Jumru 5s – A Game Engine for Serious Games.- Music and Sound Effects.- Serious Music Game Design and Testing.- Immersive Composition for Sensory Rehabilitation: 3D Visualisation, Surround Sound, and Synthesised Music to Provoke Catharsis and Healing.- Games for Other Purposes.- Gaming the Future of the Ocean: The Marine Spatial Planning Challenge 2050.- Off the Beaten Track! The Infinite Scotland Serious Game Design Approach.- Measuring the Commercial Outcomes of Serious Games in Companies – A Review.- Designing and Testing a Racing Car Serious Game Module.- The Construction of Serious Games Supporting Creativity in Student Labs.- Game Design and Theories.- The Choice of Serious Games and Gamification - A Case Study to Illustrate Key Differences.

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Book SynopsisThis book reports on the latest advances in the modeling, analysis and efficient management of information in Internet of Things (IoT) applications in the context of 5G access technologies. It presents cutting-edge applications made possible by the implementation of femtocell networks and millimeter wave communications solutions, examining them from the perspective of the universally and constantly connected IoT. Moreover, it describes novel architectural approaches to the IoT and presents the new framework possibilities offered by 5G mobile networks, including middleware requirements, node-centrality and the location of extensive functionalities at the edge. By providing researchers and professionals with a timely snapshot of emerging mobile communication systems, and highlighting the main pitfalls and potential solutions, the book fills an important gap in the literature and will foster the further developments of 5G hosting IoT devices.Table of ContentsTowards the Usage of CCN for IoT Networks.- On the Track of 5G Radio Access Network for IoT Wireless Spectrum Sharing in Device Positioning Applications.- Millimetre Wave Communication for 5G IoT Applications.- Implementing Internet of Things (IoT) Using Cognitive Radio Capabilities in 5G Mobile Networks.- Role Coordination in Large-Scale and Highly-Dense Internet-of-Things.- Energy Harvesting and Sustainable M2M Communication in 5G Mobile Technologies.- Green 5G Femtocells for Supporting Indoor Generated IoT Traffic.- On the Research and Development of Social Internet of Things.- Microgrid State Estimation Using the IoT with 5G Technology.- Building IoT Ecosystems from Mobile Clouds at Network Edge.- Middleware Platform for Mobile Crowd-Sensing Applications Using HTML5 Apis and Web Technologies.- Identification and Access to Objects and Services in the IoT Environment.- A Generic and Scalable IoT Data Fusion Infrastructure.- ONSIDE-SELF: A Selfish Node Detection and Incentive Mechanism for Opportunistic Dissemination in Future Wireless Network.- Middleware Technology for IoT Systems: Challenges and Perspectives Toward 5G.- Security in Smart Grids and Smart Spaces for Smooth IoT Deployment in 5G.- Security Challenges in 5G-based IoT Middleware Systems.- Signal Processing Techniques for Energy Efficiency, Security, and Reliability in the IoT Domain.- IoT Enablers and their Security and Privacy Issues.

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Book SynopsisOriginally published in 1981, this was the first textbook on programming in the Prolog language and is still the definitive introductory text on Prolog. Though many Prolog textbooks have been published since, this one has withstood the test of time because of its comprehensiveness, tutorial approach, and emphasis on general programming applications. Prolog has continued to attract a great deal of interest in the computer science community, and has turned out to be a basis for an important new generation of programming languages and systems for Artificial Intelligence. Since the previous edition of Programming in Prolog, the language has been standardised by the International Organization for Standardization (ISO) and this book has been updated accordingly. The authors have also introduced some new material, clarified some explanations, corrected a number of minor errors, and removed appendices about Prolog systems that are now obsolete.Trade ReviewFrom the reviews of the fifth edition: "This is the fifth and the most recent edition of a legendary book … . It was probably the first introductory Prolog book and it is still the most gentle introduction to Prolog for everyone, including non-computer scientists. … the book is as great as ever as an introductory text for Prolog. When a newbie asks for an introduction to Prolog, the best advice is still Clocksin & Mellish." (Bart Demoen, TLP-Theory and Practice of Logic Programming, Vol. 5 (3), 2005)Table of Contents1 Tutorial Introduction.- Gives the student a feel for what it is like to program in Prolog. Introduces objects, relationships, facts, rules, variables.- 1.1 Prolog.- 1.2 Objects and Relationships.- 1.3 Programming.- 1.4 Facts.- 1.5 Questions.- 1.6 Variables.- 1.7 Conjunctions.- 1.8 Rules.- 1.9 Summary and Exercises.- 2 A Closer Look.- More detailed presentation of Prolog syntax and data structures.- 2.1 Syntax.- 2.1.1 Constants.- 2.1.2 Variables.- 2.1.3 Structures.- 2.2 Characters.- 2.3 Operators.- 2.4 Equality and Unification.- 2.5 Arithmetic.- 2.6 Summary of Satisfying Goals.- 2.6.1 Successful satisfaction of a conjunction of goals.- 2.6.2 Consideration of goals in backtracking.- 2.6.3 Unification.- 3 Using Data Structures.- Representing objects and relationships by using trees and lists. Developing several standard Prolog programming techniques.- 3.1 Structures and Trees.- 3.2 Lists.- 3.3 Recursive Search.- 3.4 Mapping.- 3.5 Recursive Comparison.- 3.6 Joining Structures Together.- 3.7 Accumulators.- 3.8 Difference Structures.- 4 Backtracking and the “Cut”.- How a set of clauses generates a set of solutions. Using “cut” to modify the control sequence of running Prolog programs.- 4.1 Generating Multiple Solutions.- 4.2 The “Cut”.- 4.3 Common Uses of the Cut.- 4.3.1 Confirming the Choice of a Rule.- 4.3.2 The “cut-fail” Combination.- 4.3.3 Terminating a “generate and test”.- 4.4 Problems with the Cut.- 5 Input and Output.- Facilities available for the input and output of characters and structures. Developing a program to read sentences from the user and represent the structure as a list of words, which can be used with the Grammar Rules of Chapter.- 5.1 Reading and Writing Terms.- 5.1.1 Reading Terms.- 5.1.2 Writing Terms.- 5.2 Reading and Writing Characters.- 5.2.1 Reading Characters.- 5.2.2 Writing Characters.- 5.3 Reading English Sentences.- 5.4 Reading and Writing Files.- 5.4.1 Opening and closing streams.- 5.4.2 Changing the current input and output.- 5.4.3 Consulting.- 5.5 DeclaringOperators.- 6 Built-in Predicates.- Definition of the “core” built-in predicates, with sensible examples of how each one is used. By this point, the reader should be able to read reasonably complex programs, and should therefore be able to absorb the built-in predicates by seeing them in use.- 6.1 EnteringNew Clauses.- 6.2 Success and Failure.- 6.3 Classifying Terms.- 6.4 Treating Clauses as Terms.- 6.5 Constructing and Accessing Components of Structures.- 6.6 Affecting Backtracking.- 6.7 Constructing Compound Goals.- 6.8 Equality.- 6.9 Input and Output.- 6.10 Handling Files.- 6.11 Evaluating Arithmetic Expressions.- 6.12 Comparing Terms.- 6.13 Watching Prolog atWork.- 7 More Example Programs.- Many example programs are given, covering a wide range of interests. Examples include list processing, set operations, symbolic differentiation and simplification of formula.- 7.1 A Sorted Tree Dictionary.- 7.2 Searching a Maze.- 7.3 The Towers of Hanoi.- 7.4 Parts Inventory.- 7.5 List Processing.- 7.6 Representing andManipulating Sets.- 7.7 Sorting.- 7.8 Using the Database.- 7.8.1 Random.- 7.8.2 Gensym.- 7.8.3 Findall.- 7.9 SearchingGraphs.- 7.10 Sift the Two’s and Sift the Three’s.- 7.11 Symbolic Differentiation.- 7.12 Mapping Structures and Transforming Trees.- 7.13 Manipulating Programs.- 7.14 Bibliographic Notes.- 8 Debugging Prolog Programs.- By this point, the reader will be able to write reasonable programs, and so the problem of debugging will be relevant. Flow of control model, hints about common bugs, techniques of debugging..- 8.1 Laying out Programs.- 8.2 Common Errors.- 8.3 The Tracing Model.- 8.4 Tracing and Spy Points.- 8.4.1 Examining the Goal.- 8.4.2 Examining the Ancestors.- 8.4.3 Altering the Degree of Tracing.- 8.4.4 Altering the Satisfaction of the Goal.- 8.4.5 Other Options.- 8.4.6 Summary.- 8.5 Fixing Bugs.- 9 Using Prolog Grammar Rules.- Applications of existing techniques. Using Grammar Rules. Examining the design decisions for some aspects of analysing natural language with Grammar Rules.- 9.1 The Parsing Problem.- 9.2 Representing the Parsing Problemin Prolog.- 9.3 The Grammar Rule Notation.- 9.4 Adding ExtraArguments.- 9.5 Adding Extra Tests.- 9.6 Summary.- 9.7 Translating Language into Logic.- 9.8 More General Use of Grammar Rules.- 10 The Relation of Prolog to Logic.- Predicate Calculus, clausal form, resolution theorem proving, logic programming.- 10.1 Brief Introduction to Predicate Calculus.- 10.2 Clausal Form.- 10.3 A Notation for Clauses.- 10.4 Resolution and Proving Theorems.- 10.5 Horn Clauses.- 10.6 Prolog.- 10.7 Prolog and Logic Programming.- 11 Projects in Prolog.- A selection of suggested exercises, projects and problems.- 11.1 Easier Projects.- 11.2 Advanced Projects.- A Answers to Selected Exercises.- B Clausal Form Program Listings.- C Writing Portable Standard Prolog Programs.- The Prolog standard, writing portable programs and dealing with different Prolog implementations.- C.1 Standard Prolog for Portability.- C.2 Different Prolog Implementations.- C.3 Issues to LookOut For.- C.4 Definitions of some Standard Predicates.- C.4.1 Character Processing.- C.4.2 Directives.- C.4.3 Stream Input/Output.- C.4.4 Miscellaneous.- D CodetoSupport DCGs.- D.1 DCG Support Code.

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Book SynopsisTable of Contents1 Einleitung: Ein Blick zurück.- I: Bestandsaufnahme.- 2 Problemlösen in komplexen, computersimulierten Realitätsbereichen: Eine Übersicht.- 2.1 Der erste Impuls: „TANALAND“.- 2.2 Der Höhepunkt: „LOHHAUSEN“.- 2.3 Nebenschauplatz Testintelligenz: „TAILORSHOP“.- 2.4 Folgestudien.- 3 Thesen zur gegenwärtigen Lage der Forschung zum komplexen Problemlösen.- 3.1 Die These von der Theoriearmut.- 3.2 Die These von der mangelnden Integration anderer psychologischer Teildisziplinen.- 3.3 Die These von der fehlenden fachübergreifenden Kooperation.- 3.4 Die These von der unzureichenden Nutzung des systemtheoretischen Ansatzes.- 3.4.1 Komplexität.- 3.4.2 Vernetztheit.- 3.4.3 Stabilität und Katastrophe.- 3.5 Die These vom Fehlen einer Taxonomie von Problembzw. Systemtypen.- 3.6 Die These von der mangelhaften Beachtung des Meßfehlers.- 3.7 Die These von der suboptimalen Versuchsplanung und -auswertung.- 3.8 Die These von der unzureichenden Bestimmung der Lösungsgüte.- 3.9 Nachbemerkung zu den Thesen.- II: Das Beispiel „TAILORSHOP“.- 4 Das Simulationsprogramm „TAILORSHOP“.- 4.1 Beschreibung des Computerprogramms.- 4.2 Eine Simulationsstudie zum Simulationsprogramm.- 4.3 Bisherige Studien zum „TAILORSHOP“.- 4.4 Zur Kontroverse um die Interpretation der Ergebnisse.- III: Weiterführende Perspektiven für eine Theorie des Umgangs mit dynamischen Systemen.- 5 Problemlösen als Konstruktion von Kausalmodellen.- 5.1 Ein Begriffsinventar für Kausalmodelle in der Denkpsychologie.- 5.2 Demonstration einiger typischer Formen von autoregressiven Prozessen.- 5.3 Konstruktion von Kausalmodellen: Eine Fallstudie.- 5.4 Anwendungsmöglichkeiten.- 5.5 Bausteine für eine Theorie der Konstruktion von Kausalmodellen über dynamische Prozesse.- 5.6 Abschließende Bemerkungen zum vorgestellten Rahmenkonzept.- 6 Abschluß: Ein Blick nach vorne.- 6.1 Rahmenmodell.- 6.2 Eine Heuristik zum Verständnis des Vp-Verhaltens.- 6.3 Systematisierung der Forschungsaufgaben.- Literatur.- Register.

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Book SynopsisAISB91 is the eighth conference organized by the Society for the Study of Artificial Intelligence and Simulation of Behaviour. It is not only the oldest regular conference in Europe on AI - which spawned the ECAI conferences in 1982 - but it is also the conference that has a tradition for focusing on research as opposed to applications. The 1991 edition of the conference was no different in this respect. On the contrary, research, and particularly newly emerging research dir­ ections such as knowledge level expert systems research, neural networks and emergent functionality in autonomous agents, was strongly emphasised. The conference was organized around the following sessions: dis­ tributed intelligent agents, situatedness and emergence in autonomous agents, new modes of reasoning, the knowledge level perspective, and theorem proving and machine learning. Each of these sessions is discussed below in more detail. DISTRIBUTED INTELLIGENT AGENTS Research in distributed AI is concerned with the problem of how multiple agents and societies of agents can be organized to co-operate and collectively solve a problem. The first paper by Chakravarty (MIT) focuses on the problem of evolving agents in the context of Minsky's society of mind theory. It addesses the question of how new agents can be formed by transforming existing ones and illustrates the theory with an example from game playing. Smieja (GMD, Germany) focuses on the problem of organizing networks of agents which consist internally of neural networks.Table of ContentsDistributed Intelligent Agents.- Deriving Transformers from Knowledge Organized as a Society of Agents.- Multiple Network Systems (MINOS) Modules: Task Division and Module Discrimination.- Commitments and Projects.- RR - An Intelligent Resource-Bounded Reasoner.- Situatedness and Emergence in Autonomous Agents.- A Cognitive Model of Goal-oriented Automatisms and Breakdowns.- The ‘Logical Omniscience’ of Reactive Systems.- A Connectionist Semantics for Spatial Descriptions.- Neural Networks and Visual Behaviour: Flies, Panned Eyes, and Statistics.- Specifying Complex Behaviour for Computer Agents.- New Modes of Reasoning.- Integrating Neural Network and Expert Reasoning: An Example.- An Architecture for Selective Forgetting.- Constraint Propagation in Qualitative Modelling: Domain Variables Improve Diagnostic Efficiency.- Recursive Plans.- The Knowledge Level Perspective.- Task Centered Representation for Expert Systems at the Knowledge Level.- Knowledgeable knowledge acquisition.- Formalization of the KADS Interpretation Models.- Qualitative Models for Simulation and Control of Dynamic Systems.- Tractable Rationality at the Knowledge Level.- On Problems with the Knowledge Level Perspective.- Theorem Proving.- Using Abstraction.- Sound Substitution into Modal Contexts.- Machine Learning.- Modelling Representations of Device Knowledge in SOAR.- Instance-Based and Generalization-Based Learning Procedures Applied to Solving Integration Problems.

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Book SynopsisDuring the last decades completely new technologies for high speed railway vehicles have been developed. The primary goals have been to increase traction, axle load, and travelling speed, and to guarantee the safety of the passengers. However, new developments have revealed new limitations: settlement and destruction of the ballast and the subgrade lead to deterioration of the track; irregular wear of the wheels causes an increase in overall load and deterioration in passenger comfort; and damage of the running surfaces of the rail and the wheel is becoming more frequent. These problems have been investigated in the Priority Programme SPP 1015 supported by the Deutsche Forschungsgemeinschaft (DFG), with the goal of better understanding of the dynamic interaction of vehicle and track, and the long-term behavior of the components of the system. The book contains the scientific results of the programme as presented at the concluding colloquium held at University of Stuttgart, Germany, 2002.Table of ContentsThe DFG Priority Programme ’System Dynamics and Long-Term Behaviour of Vehicle, Track and Subgrade’.- Invited Lectures.- Vehicle/Track Interaction Optimisation within Spoornet.- Active Suspension Technology and its Effect upon Vehicle-Track Interaction.- Rolling-Contact-Fatigue and Wear of Rails: Economic and Technical Aspects.- Vehicle Dynamics.- System Dynamics of Railcars with Radial- and Lateralelastic Wheels.- Distributed Numerical Calculations of Wear in the Wheel-Rail Contact.- Modeling and Simulation of the Mid-Frequency Behaviour of an Elastic Bogie.- Wavy Wear Pattern on the Tread of Railway Wheels.- Rotor Dynamics and Irregular Wear of Elastic Wheelsets.- Contact, Friction, Wear.- On the Numerical Analysis of the Wheel-Rail System in Rolling Contact.- Experimental Analysis of the Cyclic Deformation and Damage Behavior of Characteristic Wheel and Rail Steels.- Friction and Wear of Tractive Rolling Contacts.- Model-Based Validation within the Rail-Wheel-Subgrade Modeling.- Track Dynamics.- Monitoring the Dynamics of Railway Tracks by Means of the Karhunen—Loève—Transformation.- Combined Modelling of Discretely Supported Track Models and Subgrade Models — Vertical and Lateral Dynamics.- Measurement and Modelling of Resilient Rubber Rail-Pads.- Model-Based Investigation of the Dynamic Behaviour of Railway Ballast.- The Dynamics of Railway Track and Subgrade with Respect to Deteriorated Sleeper Support.- Subgrade Dynamics.- Numerical Model and Laboratory Tests on Settlement of Ballast Track.- Track Settlement Due to Cyclic Loading with Low Minimum Pressure and Vibrations.- Simulation of the Dynamic Behavior of Bedding-Foundation-Soil in the Time Domain.- Dynamic Behavior of Railway Track Systems Analyzed in Frequency Domain.- Experimental and Numerical Investigations on the Track Stability.- Experimental Investigation and Numerical Modelling of Soils and Ballast under Cyclic and Dynamic Loading.- 3D-Simulation of Dynamic Interaction Between Track and Layered Subground.- Rigid Body Dynamics of Railway Ballast.- A Comparative Study of Results from Numerical Track-Subsoil Calculations.

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Book SynopsisNeural networks are a computing paradigm that is finding increasing attention among computer scientists. In this book, theoretical laws and models previously scattered in the literature are brought together into a general theory of artificial neural nets. Always with a view to biology and starting with the simplest nets, it is shown how the properties of models change when more general computing elements and net topologies are introduced. Each chapter contains examples, numerous illustrations, and a bibliography. The book is aimed at readers who seek an overview of the field or who wish to deepen their knowledge. It is suitable as a basis for university courses in neurocomputing.Trade Review"If you want a systematic and thorough overview of neural networks, need a good reference book on this subject, or are giving or taking a course on neural networks, this book is for you." Computing ReviewsTable of Contents1. The Biological Paradigm.- 1.1 Neural computation.- 1.1.1 Natural and artificial neural networks.- 1.1.2 Models of computation.- 1.1.3 Elements of a computing model.- 1.2 Networks of neurons.- 1.2.1 Structure of the neurons.- 1.2.2 Transmission of information.- 1.2.3 Information processing at the neurons and synapses.- 1.2.4 Storage of information — learning.- 1.2.5 The neuron — a self-organizing system.- 1.3 Artificial neural networks.- 1.3.1 Networks of primitive functions.- 1.3.2 Approximation of functions.- 1.3.3 Caveat.- 1.4 Historical and bibliographical remarks.- 2. Threshold Logic.- 2.1 Networks of functions.- 2.1.1 Feed-forward and recurrent networks.- 2.1.2 The computing units.- 2.2 Synthesis of Boolean functions.- 2.2.1 Conjunction, disjunction, negation.- 2.2.2 Geometric interpretation.- 2.2.3 Constructive synthesis.- 2.3 Equivalent networks.- 2.3.1 Weighted and unweighted networks.- 2.3.2 Absolute and relative inhibition.- 2.3.3 Binary signals and pulse coding.- 2.4 Recurrent networks.- 2.4.1 Stored state networks.- 2.4.2 Finite automata.- 2.4.3 Finite automata and recurrent networks.- 2.4.4 A first classification of neural networks.- 2.5 Harmonic analysis of logical functions.- 2.5.1 General expression.- 2.5.2 The Hadamard—Walsh transform.- 2.5.3 Applications of threshold logic.- 2.6 Historical and bibliographical remarks.- 3.Weighted Networks — The Perceptron.- 3.1 Perceptrons and parallel processing.- 3.1.1 Perceptrons as weighted threshold elements.- 3.1.2 Computational limits of the perceptron model.- 3.2 Implementation of logical functions.- 3.2.1 Geometric interpretation.- 3.2.2 The XOR problem.- 3.3 Linearly separable functions.- 3.3.1 Linear separability.- 3.3.2 Duality of input space and weight space.- 3.3.3 The error function in weight space.- 3.3.4 General decision curves.- 3.4 Applications and biological analogy.- 3.4.1 Edge detection with perceptrons.- 3.4.2 The structure of the retina.- 3.4.3 Pyramidal networks and the neocognitron.- 3.4.4 The silicon retina.- 3.5 Historical and bibliographical remarks.- 4. Perceptron Learning.- 4.1 Learning algorithms for neural networks.- 4.1.1 Classes of learning algorithms.- 4.1.2 Vector notation.- 4.1.3 Absolute linear separability.- 4.1.4 The error surface and the search method.- 4.2 Algorithmic learning.- 4.2.1 Geometric visualization.- 4.2.2 Convergence of the algorithm.- 4.2.3 Accelerating convergence.- 4.2.4 The pocket algorithm.- 4.2.5 Complexity of perceptron learning.- 4.3 Linear programming.- 4.3.1 Inner points of polytopes.- 4.3.2 Linear separability as linear optimization.- 4.3.3 Karmarkar’s algorithm.- 4.4 Historical and bibliographical remarks.- 5. Unsupervised Learning and Clustering Algorithms.- 5.1 Competitive learning.- 5.1.1 Generalization of the perceptron problem.- 5.1.2 Unsupervised learning through competition.- 5.2 Convergence analysis.- 5.2.1 The one-dimensional case — energy function.- 5.2.2 Multidimensional case — the classical methods.- 5.2.3 Unsupervised learning as minimization problem.- 5.2.4 Stability of the solutions.- 5.3 Principal component analysis.- 5.3.1 Unsupervised reinforcement learning.- 5.3.2 Convergence of the learning algorithm.- 5.3.3 Multiple principal components.- 5.4 Some applications.- 5.4.1 Pattern recognition.- 5.4.2 Image compression.- 5.5 Historical and bibliographical remarks.- 6. One and Two Layered Networks.- 6.1 Structure and geometric visualization.- 6.1.1 Network architecture.- 6.1.2 The XOR problem revisited.- 6.1.3 Geometric visualization.- 6.2 Counting regions in input and weight space.- 6.2.1 Weight space regions for the XOR problem.- 6.2.2 Bipolar vectors.- 6.2.3 Projection of the solution regions.- 6.2.4 Geometric interpretation.- 6.3 Regions for two layered networks.- 6.3.1 Regions in weight space for the XOR problem.- 6.3.2 Number of regions in general.- 6.3.3 Consequences.- 6.3.4 The Vapnik—Chervonenkis dimension.- 6.3.5 The problem of local minima.- 6.4 Historical and bibliographical remarks.- 7. The Backpropagation Algorithm.- 7.1 Learning as gradient descent.- 7.1.1 Differentiable activation functions.- 7.1.2 Regions in input space.- 7.1.3 Local minima of the error function.- 7.2 General feed-forward networks.- 7.2.1 The learning problem.- 7.2.2 Derivatives of network functions.- 7.2.3 Steps of the backpropagation algorithm.- 7.2.4 Learning with backpropagation.- 7.3 The case of layered networks.- 7.3.1 Extended network.- 7.3.2 Steps of the algorithm.- 7.3.3 Backpropagation in matrix form.- 7.3.4 The locality of backpropagation.- 7.3.5 Error during training.- 7.4 Recurrent networks.- 7.4.1 Backpropagation through time.- 7.4.2 Hidden Markov Models.- 7.4.3 Variational problems.- 7.5 Historical and bibliographical remarks.- 8. Fast Learning Algorithms.- 8.1 Introduction — classical backpropagation.- 8.1.1 Backpropagation with momentum.- 8.1.2 The fractal geometry of backpropagation.- 8.2 Some simple improvements to backpropagation.- 8.2.1 Initial weight selection.- 8.2.2 Clipped derivatives and offset term.- 8.2.3 Reducing the number of floating-point operations.- 8.2.4 Data decorrelation.- 8.3 Adaptive step algorithms.- 8.3.1 Silva and Almeida’s algorithm.- 8.3.2 Delta-bar-delta.- 8.3.3 Rprop.- 8.3.4 The Dynamic Adaption algorithm.- 8.4 Second-order algorithms.- 8.4.1 Quickprop.- 8.4.2 QRprop.- 8.4.3 Second-order backpropagation.- 8.5 Relaxation methods.- 8.5.1 Weight and node perturbation.- 8.5.2 Symmetric and asymmetric relaxation.- 8.5.3 A final thought on taxonomy.- 8.6 Historical and bibliographical remarks.- 9. Statistics and Neural Networks.- 9.1 Linear and nonlinear regression.- 9.1.1 The problem of good generalization.- 9.1.2 Linear regression.- 9.1.3 Nonlinear units.- 9.1.4 Computing the prediction error.- 9.1.5 The jackknife and cross-validation.- 9.1.6 Committees of networks.- 9.2 Multiple regression.- 9.2.1 Visualization of the solution regions.- 9.2.2 Linear equations and the pseudoinverse.- 9.2.3 The hidden layer.- 9.2.4 Computation of the pseudoinverse.- 9.3 Classification networks.- 9.3.1 An application: NETtalk.- 9.3.2 The Bayes property of classifier networks.- 9.3.3 Connectionist speech recognition.- 9.3.4 Autoregressive models for time series analysis.- 9.4 Historical and bibliographical remarks.- 10. The Complexity of Learning.- 10.1 Network functions.- 10.1.1 Learning algorithms for multilayer networks.- 10.1.2 Hilbert’s problem and computability.- 10.1.3 Kolmogorov’s theorem.- 10.2 Function approximation.- 10.2.1 The one-dimensional case.- 10.2.2 The multidimensional case.- 10.3 Complexity of learning problems.- 10.3.1 Complexity classes.- 10.3.2 NP-complete learning problems.- 10.3.3 Complexity of learning with AND-OR networks.- 10.3.4 Simplifications of the network architecture.- 10.3.5 Learning with hints.- 10.4 Historical and bibliographical remarks.- 11. Fuzzy Logic.- 11.1 Fuzzy sets and fuzzy logic.- 11.1.1 Imprecise data and imprecise rules.- 11.1.2 The fuzzy set concept.- 11.1.3 Geometric representation of fuzzy sets.- 11.1.4 Fuzzy set theory, logic operators, and geometry.- 11.1.5 Families of fuzzy operators.- 11.2 Fuzzy inferences.- 11.2.1 Inferences from imprecise data.- 11.2.2 Fuzzy numbers and inverse operation.- 11.3 Control with fuzzy logic.- 11.3.1 Fuzzy controllers.- 11.3.2 Fuzzy networks.- 11.3.3 Function approximation with fuzzy methods.- 11.3.4 The eye as a fuzzy system — color vision.- 11.4 Historical and bibliographical remarks.- 12. Associative Networks.- 12.1 Associative pattern recognition.- 12.1.1 Recurrent networks and types of associative memories.- 12.1.2 Structure of an associative memory.- 12.1.3 The eigenvector automaton.- 12.2 Associative learning.- 12.2.1 Hebbian learning — the correlation matrix.- 12.2.2 Geometric interpretation of Hebbian learning.- 12.2.3 Networks as dynamical systems — some experiments.- 12.2.4 Another visualization.- 12.3 The capacity problem.- 12.4 The pseudoinverse.- 12.4.1 Definition and properties of the pseudoinverse.- 12.4.2 Orthogonal projections.- 12.4.3 Holographic memories.- 12.4.4 Translation invariant pattern recognition.- 12.5 Historical and bibliographical remarks.- 13. The Hopfield Model.- 13.1 Synchronous and asynchronous networks.- 13.1.1 Recursive networks with stochastic dynamics.- 13.1.2 The bidirectional associative memory.- 13.1.3 The energy function.- 13.2 Definition of Hopfield networks.- 13.2.1 Asynchronous networks.- 13.2.2 Examples of the model.- 13.2.3 Isomorphism between the Hopfield and Ising models.- 13.3 Converge to stable states.- 13.3.1 Dynamics of Hopfield networks.- 13.3.2 Covergence proof.- 13.3.3 Hebbian learning.- 13.4 Equivalence of Hopfield and perceptron learning.- 13.4.1 Perceptron learning in Hopfield networks.- 13.4.2 Complexity of learning in Hopfield models.- 13.5 Parallel combinatorics.- 13.5.1 NP-complete problems and massive parallelism.- 13.5.2 The multiflop problem.- 13.5.3 The eight rooks problem.- 13.5.4 The eight queens problem.- 13.5.5 The traveling salesman.- 13.5.6 The limits of Hopfield networks.- 13.6 Implementation of Hopfield networks.- 13.6.1 Electrical implementation.- 13.6.2 Optical implementation.- 13.7 Historical and bibliographical remarks.- 14. Stochastic Networks.- 14.1 Variations of the Hopfield model.- 14.1.1 The continuous model.- 14.2 Stochastic systems.- 14.2.1 Simulated annealing.- 14.2.2 Stochastic neural networks.- 14.2.3 Markov chains.- 14.2.4 The Boltzmann distribution.- 14.2.5 Physical meaning of the Boltzmann distribution.- 14.3 Learning algorithms and applications.- 14.3.1 Boltzmann learning.- 14.3.2 Combinatorial optimization.- 14.4 Historical and bibliographical remarks.- 15. Kohonen Networks.- 15.1 Self-organization.- 15.1.1 Charting input space.- 15.1.2 Topology preserving maps in the brain.- 15.2 Kohonen’s model.- 15.2.1 Learning algorithm.- 15.2.2 Mapping high-dimensional spaces.- 15.3 Analysis of convergence.- 15.3.1 Potential function — the one-dimensional case.- 15.3.2 The two-dimensional case.- 15.3.3 Effect of a unit’s neighborhood.- 15.3.4 Metastable states.- 15.3.5 What dimension for Kohonen networks?.- 15.4 Applications.- 15.4.1 Approximation of functions.- 15.4.2 Inverse kinematics.- 15.5 Historical and bibliographical remarks.- 16. Modular Neural Networks.- 16.1 Constructive algorithms for modular networks.- 16.1.1 Cascade correlation.- 16.1.2 Optimal modules and mixtures of experts.- 16.2 Hybrid networks.- 16.2.1 The ART architectures.- 16.2.2 Maximum entropy.- 16.2.3 Counterpropagation networks.- 16.2.4 Spline networks.- 16.2.5 Radial basis functions.- 16.3 Historical and bibliographical remarks.- 17. Genetic Algorithms.- 17.1 Coding and operators.- 17.1.1 Optimization problems.- 17.1.2 Methods of stochastic optimization.- 17.1.3 Genetic coding.- 17.1.4 Information exchange with genetic operators.- 17.2 Properties of genetic algorithms.- 17.2.1 Convergence analysis.- 17.2.2 Deceptive problems.- 17.2.3 Genetic drift.- 17.2.4 Gradient methods versus genetic algorithms.- 17.3 Neural networks and genetic algorithms.- 17.3.1 The problem of symmetries.- 17.3.2 A numerical experiment.- 17.3.3 Other applications of GAs.- 17.4 Historical and bibliographical remarks.- 18. Hardware for Neural Networks.- 18.1 Taxonomy of neural hardware.- 18.1.1 Performance requirements.- 18.1.2 Types of neurocomputers.- 18.2 Analog neural networks.- 18.2.1 Coding.- 18.2.2 VLSI transistor circuits.- 18.2.3 Transistors with stored charge.- 18.2.4 CCD components.- 18.3 Digital networks.- 18.3.1 Numerical representation of weights and signals.- 18.3.2 Vector and signal processors.- 18.3.3 Systolic arrays.- 18.3.4 One-dimensional structures.- 18.4 Innovative computer architectures.- 18.4.1 VLSI microprocessors for neural networks.- 18.4.2 Optical computers.- 18.4.3 Pulse coded networks.- 18.5 Historical and bibliographical remarks.

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Book SynopsisThis book presents the state of the art of artificial intelligence techniques applied to structural engineering. The 28 revised full papers by leading scientists were solicited for presentation at a meeting held in Ascona, Switzerland, in July 1998.The recent advances in information technology, in particular decreasing hardware cost, Internet communication, faster computation, increased bandwidth, etc., allow for the application of new AI techniques to structural engineering. The papers presented deal with new aspects of information technology support for the design, analysis, monitoring, control and diagnosis of various structural engineering systems.Table of ContentsStructural monitoring of civil structures using vibration measurement Current practice and future.- Object-oriented software patterns for engineering design standards processing.- Design and verification of real-time systems.- Using knowledge nodes for knowledge discovery and collaboration.- Heating system design support.- Collaborative desktop engineering.- Towards personalized structural engineering tools.- Complex systems: Why do they need to evolve and how can evolution be supported.- Formalizing product model transformations: Case examples and applications.- Internet-based web-mediated collaborative design and learning environment.- Wearable computers for field inspectors: Delivering data and knowledge-based support in the field.- Conceptual designing as a sequence of situated acts.- Some personal experience in computer aided engineering research.- Knowledge discovery from multimedia case libraries.- Customisable knowledge bases for conceptual design.- Articulate design of free-form structures.- Applying quantitative constraint satisfaction in preliminary design.- Agents in computer-assisted collaborative design.- A collaborative negotiation methodology for large scale civil engineering and architectural projects.- An investigation into the integration of neural networks with the structured genetic algorithm to aid conceptual design.- Finding the right model for bridge diagnosis.- Knowledge-based assistants in collaborative engineering.- CAD modelling in multidisciplinary design domains.- A family of software components to deliver solutions for the interpretation of monitoring data.- AI methods in concurrent engineering.- A new collaborative design environment for engineers and architects.- Intelligent structures: A new direction in structural control.- Integration of expert systems in a structural design office.- Teaching knowledge engineering: Experiences.- Design support for viaducts.- Converting function into object.- Software agent techniques in design.- Case-based design process facilitating collaboration and information evolution.- Shared experiences: Management of experiential knowledge in the building industry.- Dam safety: Improving management.- Integrating virtual reality and telepresence to remotely monitor construction sites: A ViRTUE project.- Proposal for 4.5 dimensional design via product models and expert system.- A product information system based on dynamic classification.- Structural monitoring: Decision-support through multiple data interpretations.- Augmented reality applications to structural monitoring.- Analysis and design of the as-built model.- On theoretical backgrounds of CAD.

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Book SynopsisWritten with graduate and advanced undergraduate students in mind, this textbook introduces computational logic from the foundations of first-order logic to state-of-the-art decision procedures for arithmetic, data structures, and combination theories. The textbook also presents a logical approach to engineering correct software. Verification exercises are given to develop the reader's facility in specifying and verifying software using logic. The treatment of verification concludes with an introduction to the static analysis of software, an important component of modern verification systems. The final chapter outlines courses of further study.Trade Review"...this book, which addresses the verification of sequential programs, exhibits all the features of a field that has finally fully matured. The material is substantial; it is organized very thoughtfully; the writing is concise but simple, easy to follow, and illustrated with ample examples... Overall, this book is very well written, thoughtfully constructed, and substantive yet accessible. It is bound to become a standard textbook in program verification." (Fatma Mill, ACM Computing Reviews, August 2008)Table of ContentsFoundations.- Propositional Logic.- First-Order Logic.- First-Order Theories.- Induction.- Program Correctness: Mechanics.- Program Correctness: Strategies.- Algorithmic Reasoning.- Quantified Linear Arithmetic.- Quantifier-Free Linear Arithmetic.- Quantifier-Free Equality and Data Structures.- Combining Decision Procedures.- Arrays.- Invariant Generation.- Further Reading.

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Book SynopsisFinite element methods have become ever more important to engineers as tools for design and optimization, now even for solving non-linear technological problems. However, several aspects must be considered for finite-element simulations which are specific for non-linear problems: These problems require the knowledge and the understanding of theoretical foundations and their finite-element discretization as well as algorithms for solving the non-linear equations. This book provides the reader with the required knowledge covering the complete field of finite element analyses in solid mechanics. It is written for advanced students in engineering fields but serves also as an introduction into non-linear simulation for the practising engineer.Trade ReviewFrom the reviews: "This book describes, besides the physical and mathematical background of finite element method (FEM), special discretization techniques and algorithms which have to be applied to nonlinear problems of solid mechanics. … The book is intended for graduate students of mechanical and civil engineering who want to familiarize themselves with numerical methods applied to problems in solid mechanics. This book applies also to PhD students and engineers working in industry who need further background information on the application of finite elements to nonlinear problems." (Razvan Raducanu, Zentrablatt MATH, Vol. 1153, 2009) “The aim of this book is to describe ‘special discretization techniques and algorithms … for nonlinear problems of solid mechanics’. It is written primarily as a textbook for graduate students of mechanical and civil engineering … the terminology and style are also accessible to researchers with an applied mathematics background.” (Christoph Ortner, Mathematical Reviews, Issue 2010 a)Table of ContentsNonlinear Phenomena.- Basic Equations of Continuum Mechanics.- Spatial Discretization Techniques.- Solution Methods for Time Independent Problems.- Solution Methods for Time Dependent Problems.- Stability Problems.- Adaptive Methods.- Special Structural Elements.- Special Finite Elements for Continua.- Contact Problems.- Automation of the Finite Element Method by J. Korelc.

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Book SynopsisThis book offers a basic introduction to genetic algorithms. It provides a detailed explanation of genetic algorithm concepts and examines numerous genetic algorithm optimization problems. In addition, the book presents implementation of optimization problems using C and C++ as well as simulated solutions for genetic algorithm problems using MATLAB 7.0. It also includes application case studies on genetic algorithms in emerging fields.Table of ContentsEvolutionary Computation.- Genetic Algorithms.- Terminologies and Operators of GA.- Advanced Operators and Techniques in Genetic Algorithm.- Classification of Genetic Algorithm.- Genetic Programming.- Genetic Algorithm Optimization Problems.- Genetic Algorithm Implementation Using Matlab.- Genetic Algorithm Optimization in C/C++.- Applications of Genetic Algorithms.- to Particle Swarm Optimization and Ant Colony Optimization.

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Book SynopsisComputational Fluid Dynamics: An Introduction grew out of a von Karman Institute (VKI) Lecture Series by the same title ?rst presented in 1985 and repeated with modi?cations every year since that time. The objective, then and now, was to present the subject of computational ?uid dynamics (CFD) to an audience unfamiliar with all but the most basic numerical techniques and to do so in such a way that the practical application of CFD would become clear to everyone. A second edition appeared in 1995 with updates to all the chapters and when that printing came to an end, the publisher requested that the editor and authors consider the preparation of a third edition. Happily, the authors received the request with enthusiasm. The third edition has the goal of presenting additional updates and clari?cations while preserving the introductory nature of the material. The book is divided into three parts. John Anderson lays out the subject in Part I by ?rst describing the governing equations of ?uid dynamics, concentrating on their mathematical properties which contain the keys to the choice of the numerical approach. Methods of discretizing the equations are discussed and transformation techniques and grids are presented. Two examples of numerical methods close out this part of the book: source and vortex panel methods and the explicit method. Part II is devoted to four self-contained chapters on more advanced material. Roger Grundmann treats the boundary layer equations and methods of solution.Trade ReviewFrom the reviews of the third edition: "The objective of the book is to introduce a reader with limited prior knowledge of numerical techniques to the computation of fluid flows. The stated goal of the third edition is to present updates and clarifications while preserving the book’s introductory nature. The focus of the book is exclusively on aeronautical applications. … this book succeeds in achieving its goal of introducing readers to CFD." (Andreas Haselbacher, AIAA Journal, Vol. 47 (7), July, 2009)Table of ContentsBasic Philosophy of CFD.- Governing Equations of Fluid Dynamics.- Incompressible Inviscid Flows: Source andVortex Panel Methods.- Mathematical Properties of the Fluid Dynamic Equations.- Discretization of Partial Differential Equations.- Transformations and Grids.- Explicit Finite Difference Methods: Some Selected Applications to Inviscid and Viscous Flows.- Boundary Layer Equations and Methods of Solution.- Implicit Time-Dependent Methods for Inviscid and Viscous Compressible Flows, with a Discussion of the Concept of Numerical Dissipation.- to Finite Element Methods in Computational Fluid Dynamics.- to Finite Volume Methods in Computational Fluid Dynamics.- Aspects of CFD Computations with Commercial Packages.

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Book SynopsisMining social networks has now becoming a very popular research area not only for data mining and web mining but also social network analysis. Data mining is a technique that has the ability to process and analyze large amount of data and by this to discover valuable information from the data. In recent year, due to the growth of social communications and social networking websites, data mining becomes a very important and powerful technique to process and analyze such large amount of data. Thus, this book will focus upon Mining and Analyzing social network. Some chapters in this book are extended from the papers that presented in MSNDS2009 (the First International Workshop on Mining Social Networks for Decision Support) and SNMABA2009 ((The International Workshop on Social Networks Mining and Analysis for Business Applications)). In addition, we also sent invitations to researchers that are famous in this research area to contribute for this book. The chapters of this book are introduced as follows: In chapter 1-Graph Model for Pattern Recognition in Text, Qin Wu et al. present a novel approach that uses a weighted directed multigraph for text pattern recognition. In the proposed methodology, a weighted directed multigraph model has been set up by using the distances between the keywords as the weights of arcs as well a keyword-frequency distance based algorithm has also been introduced. Case studies are also included in this chapter to show the performance is better than traditional means.Table of ContentsGraph Model for Pattern Recognition in Text.- Retrieving Wiki Content Using an Ontology.- Ego-Centric Network Sampling in Viral Marketing Applications.- Integrating SNA and DM Technology into HR Practice and Research: Layoff Prediction Model.- Actor Identification in Implicit Relational Data Sources.- Perception of Online Social Networks.- Ranking Learning Entities on the Web by Integrating Network-Based Features.- Discovering Proximal Social Intelligence for Quality Decision Support.- Discovering User Interests by Document Classification.- Network Analysis of Opto-Electronics Industry Cluster: A Case of Taiwan.

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Book SynopsisContext-aware ranking is an important task with many applications. E.g. in recommender systems items (products, movies, ...) and for search engines webpages should be ranked. In all these applications, the ranking is not global (i.e. always the same) but depends on the context. Simple examples for context are the user for recommender systems and the query for search engines. More complicated context includes time, last actions, etc. The major problem is that typically the variable domains (e.g. customers, products) are categorical and huge, the observations are very sparse and only positive events are observed. In this book, a generic method for context-aware ranking as well as its application are presented. For modelling a new factorization based on pairwise interactions is proposed and compared to other tensor factorization approaches. For learning, the `Bayesian Context-aware Ranking' framework consisting of an optimization criterion and algorithm is developed. The second main part of the book applies this general theory to the three scenarios of item, tag and sequential-set recommendation. Furthermore extensions of time-variant factors and one-class problems are studied. This book generalizes and builds on work that has received the `WWW 2010 Best Paper Award', the `WSDM 2010 Best Student Paper Award' and the `ECML/PKDD 2009 Best Discovery Challenge Award'.Table of ContentsPart I Overview.- Part II Theory.- Part III Application.- Part IV Extensions.- Part V Conclusion.

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