Artificial intelligence (AI) Books

Book SynopsisArtificial Intelligence and Music Ecosystem highlights the opportunities and rewards associated with the application of AI in the creative arts. Featuring an array of voices, including interviews with Jacques Attali, Holly Herndon and Scott Cohen, this book offers interdisciplinary approaches to pressing ethical and technical questions associated with AI. Considering the perspectives of developers, students and artists, as well as the wider themes of law, ethics and philosophy, Artificial Intelligence and Music Ecosystem is an essential introduction for anyone interested in the impact of AI on music, including those studying and working in the creative arts.Table of ContentsList of contributorsAcknowledgementsIntroductionChapter 1 The Future – Interview with Jacques AttaliChapter 2 AI music – On the Meaning of Music: Music is a language without a dictionary – David CopeChapter 3 The Developer – What do music software developers do? – Miller PucketteChapter 4 The Student – Shortcuts Guide To Music Theory – Artur Osipov Chapter 5 The Artist – Interview with Holly HerndonChapter 6 Robotics – Fast and Curious: A CNN for Ethical Deep Learning Musical Generation – Richard Savery & Gil WeinbergChapter 7 Extended Reality – Music in Immersive XR Environments:The Possibilities (and Approaches) for (AI) – Gareth W. Young & Aljosa SmolicChapter 8 Data – A Quantified Quickening: Data, AI and the Consumption and Composition of Music – Jennifer EdmondChapter 9 Law– You Can Call Me Hal: AI & Music IP – Martin ClancyChapter 10 Ethics – Whose Ethics? Approaches to a Equitable and Sustainable Music Ecosystem – Martin ClancyChapter 11 Global Ethics – From Philosophy to Practice A Culturally Informed Ethics of Music AI in Asia – Rujing Stacy Huang, Andre Holzapfel & Bob L. T. SturmChapter 12 Start-ups – AI: Why I Care – Mick KielyChapter 13 Music Industry – Interview with Scott CohenChapter 14 Philosophy – Amor Fati: A Theoretical Model of the Music Ecosystem – Martin ClancyIndex

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Book SynopsisThe book is based on the best papers of IEEE IRI 2018 and IEEE FMI 2018, Salt Lake City, July, 2018. They have been enhanced and modified suitably for publication. The book comprises recent works covering several aspects of reuse in intelligent systems including Scientific Theory and Technology-Based Applications. New data analytic algorithms, technologies, and tools are sought to be able to manage, integrate, and utilize large amounts of data despite hardware, software, and/or bandwidth constraints; to construct models yielding important data insights, and to create visualizations to aid in presenting and understanding the data. Furthermore, it addresses the representation, cleansing, generalization, validation, and reasoning strategies for the scientifically-sound and cost-effective advancement of all kinds of intelligent systems including all software and hardware aspects. The book addresses problems such as, how to optimally select the information/data sets for reuse and Table of ContentsExperimental Studies on the Impact of Data Sampling with Severely Imbalanced big data. How to Optimally Combine Univariate and Multivariate Feature Selection with Data Sampling for Classifying Noisy, High Dimensional and Class Imbalanced DNA Microarray Data. Big Data and Class Imbalance in Medicare Fraud Detection. Movie Recommendations Based on a Recurrent Neural Network Model. A Recommendation System Enhanced by Topic Modeling for Knowledge Reuse in MOOCs Ecosystems. Toward a Computer Vision Based Approach for Developing Algorithms for Soccer Playing Robots. Context-dependent Reachability Analysis for Hybrid Systems. Netflow Feature Evaluation for the Detection of Slow Read HTTP Attacks. Predictive Analysis of Server Log Data for Forecasting Events.

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Book SynopsisThis book examines the growing importance of algorithms and automationincluding emerging forms of artificial intelligencein the gathering, composition, and distribution of news. In it the authors connect a long line of research on journalism and computation with scholarly and professional terrain yet to be explored. Taken as a whole, these chapters share some of the noble ambitions of the pioneering publications on reporting algorithms', such as a desire to see computing help journalists in their watchdog role by holding power to account. However, they also go further, firstly by addressing the fuller range of technologies that computational journalism now consists of: from chatbots and recommender systems to artificial intelligence and atomised journalism. Secondly, they advance the literature by demonstrating the increased variety of uses for these technologies, including engaging underserved audiences, selling subscriptions, and recombining and re-using content. Thirdly, tTable of Contents1. Algorithms, Automation, and News Neil Thurman, Seth C. Lewis and Jessica Kunert2. On the Democratic Role of News Recommenders Natali Helberger3. Newsbots That Mediate Journalist and Audience Relationships Heather Ford and Jonathon Hutchinson4. Public Service Chatbots: Automating Conversation with BBC News Bronwyn Jones and Rhianne Jones5. Selling News to Audiences – A Qualitative Inquiry into the Emerging Logics of Algorithmic News Personalization in European Quality News MediaBalázs Bodó6. Making Artificial Intelligence Work for Investigative Journalism Jonathan Stray7. Human Still in the Loop. Editors Reconsider the Ideals of Professional Journalism Through Automation Marko Milosavljević and Igor Vobič8. News Algorithms, Photojournalism and the Assumption of Mechanical Objectivity in Journalism Matt Carlson9. Structured Journalism and the Semantic Units of News David Caswell10. Atomising the News: The (In)Flexibility of Structured Journalism Rhianne Jones and Bronwyn Jones11. Towards a Design Orientation on Algorithms and Automation in News Production Nicholas Diakopoulos12. Prioritizing the Audience’s View of Automation in Journalism Andrea L. Guzman

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Book SynopsisUnderstanding Geometric Algebra: Hamilton, Grassmann, and Clifford for Computer Vision and Graphics introduces geometric algebra with an emphasis on the background mathematics of Hamilton, Grassmann, and Clifford. It shows how to describe and compute geometry for 3D modeling applications in computer graphics and computer vision.Unlike similar texts, this book first gives separate descriptions of the various algebras and then explains how they are combined to define the field of geometric algebra. It starts with 3D Euclidean geometry along with discussions as to how the descriptions of geometry could be altered if using a non-orthogonal (oblique) coordinate system. The text focuses on Hamiltonâs quaternion algebra, Grassmannâs outer product algebra, and Clifford algebra that underlies the mathematical structure of geometric algebra. It also presents points and lines in 3D as objects in 4D in the projective geometry framework; explores conformal geometryTrade Review"Several software tools are available for executing geometric algebra, but the purpose of the book is to bring about a deeper insight and interest in the theory on which these tools are based."—Zentralblatt MATH 1319Table of ContentsIntroduction. 3D Euclidean Geometry. Oblique Coordinate Systems. Hamilton's Quaternion Algebra. Grassmann's Outer Product Algebra. Geometric Product and Clifford Algebra. Homogeneous Space and Grassmann-Cayley Algebra. Conformal Space and Conformal Geometry: Geometric Algebra. Camera Imaging and Conformal Transformations. Answers. Bibliography. Index.

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Book SynopsisWhat is artificial intelligence (AI)? How does AI affect death matters and the digital beyond? How are death and dying handled in our digital age?AI for Dying and Death covers a broad range of literature, research and challenges around this topic. It explores ethical memorisation, digital legacies and bereavement, post death avatars and AI and the digital beyond. It also analyzes religious perspectives on AI for death and dying, and planning for death in a digital age. Table of ContentsChapter 1 What is Artificial Intelligence?Chapter 2 Death and Dying in the Digital AgeChapter 3 Ethical MemorialisationChapter 4 Digital Legacy ManagementChapter 5 Religious PerspectivesChapter 6 Digital BereavementChapter 7 Digital Afterlife, Digital Immortal Creation and Artificial IntelligenceChapter 8 Artificial Intelligence and the Digital BeyondReferences

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Book SynopsisArtificial Intelligence has revolutionised areas of medicine. This book focuses on the integral role of AI in radiology, shedding light on how this technology can enhance patient care and streamline professional workflows.Trade Review“The book is not just about the present state of affairs. It offers a vision, exploring the future trajectories of AI in radiology, addressing challenges, controversies, and the endless possibilities on the horizon.Having witnessed Oge’s dedication and forward-thinking approach firsthand, I am confident that this book will serve as an invaluable resource. For those stepping into the realm of AI in radiology or seeking to deepen their knowledge, this book provides a holistic, scientifically rigorous, and practical guide…I wholeheartedly believe that it will stand as a cornerstone for all enthusiasts eager to delve into the world of AI in Radiology.”--Felipe Kitamura, MD, PhDDirector of Applied Innovation and AI at DasaAffiliated Professor of Radiology at Universidade Federal de São PauloTable of Contents1 Artificial Intelligence and Medicine: The Big Picture2 AI in Radiology: From Fear to Leadership3 Fundamentals of Machine Learning and Deep Learning4 Fundamentals of Medical Image Analysis5 Data: The Essential Ingredient in AI Solutions6 Clinical Applications of AI in Radiology7 Harnessing AI in Radiology Education and Training8 Getting Started with Deep Learning in Medical Imaging9 The Future of AI in Radiology10 Resources for Further Learning

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Book SynopsisArtificial intelligence (AI) has revolutionized many areas of medicine and is increasingly being embraced. This book focuses on the integral role of AI in radiology, shedding light on how this technology can enhance patient care and streamline professional workflows.This book reviews, explains, and contextualizes some of the most current, practical, and relevant developments in artificial intelligence and deep learning in radiology and medical image analysis. AI for Radiology presents a balanced viewpoint of the impact of AI in these fields, underscoring that AI technologies are not intended to replace radiologists but rather to augment their capabilities, freeing professionals to focus on more complex cases. This book guides readers from the basic principles of AI to their practical applications in radiology, moving from the role of data in AI to the ethical and regulatory considerations of using AI in radiology and concluding with a selection of resources for furtherTrade Review“The book is not just about the present state of affairs. It offers a vision, exploring the future trajectories of AI in radiology, addressing challenges, controversies, and the endless possibilities on the horizon.Having witnessed Oge’s dedication and forward-thinking approach firsthand, I am confident that this book will serve as an invaluable resource. For those stepping into the realm of AI in radiology or seeking to deepen their knowledge, this book provides a holistic, scientifically rigorous, and practical guide…I wholeheartedly believe that it will stand as a cornerstone for all enthusiasts eager to delve into the world of AI in Radiology.”--Felipe Kitamura, MD, PhDDirector of Applied Innovation and AI at DasaAffiliated Professor of Radiology at Universidade Federal de São PauloTable of Contents1 Artificial Intelligence and Medicine: The Big Picture2 AI in Radiology: From Fear to Leadership3 Fundamentals of Machine Learning and Deep Learning4 Fundamentals of Medical Image Analysis5 Data: The Essential Ingredient in AI Solutions6 Clinical Applications of AI in Radiology7 Harnessing AI in Radiology Education and Training8 Getting Started with Deep Learning in Medical Imaging9 The Future of AI in Radiology10 Resources for Further Learning

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Book SynopsisWhat is artificial intelligence? How is artificial intelligence used in game development?Game development lives in its own technical world. It has its own idioms, skills, and challenges. Thatâs one of the reasons games are so much fun to work on. Each game has its own rules, its own aesthetic, and its own trade-offs, and the hardware it will run on keeps changing. AI for Games is designed to help you understand one element of game development: artificial intelligence (AI).Table of ContentsAuthor. Introduction. 1 What Is AI? 2 Model of Game AI. 3 Algorithms and Data Structures. 4 Game AI. 5 Techniques. 6 Supporting Technologies. Index.

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Book SynopsisThis book addresses the topics related to artificial intelligence, the Internet of Things, blockchain technology, and machine learning. It brings together researchers, developers, practitioners, and users interested in cybersecurity and forensics. The first objective is to learn and understand the need for and impact of advanced cybersecurity and forensics and its implementation with multiple smart computational technologies. This objective answers why and how cybersecurity and forensics have evolved as one of the most promising and widely-accepted technologies globally and has widely-accepted applications. The second objective is to learn how to use advanced cybersecurity and forensics practices to answer computational problems where confidentiality, integrity, and availability are essential aspects to handle and answer. This book is structured in such a way so that the field of study is relevant to each readerâs major or interests. It aims to help each reader see the relevance of cybersecurity and forensics to their career or interests. This book intends to encourage researchers to develop novel theories to enrich their scholarly knowledge to achieve sustainable development and foster sustainability. Readers will gain valuable knowledge and insights about smart computing technologies using this exciting book.This book:â Includes detailed applications of cybersecurity and forensics for real-life problemsâ Addresses the challenges and solutions related to implementing cybersecurity in multiple domains of smart computational technologies â Includes the latest trends and areas of research in cybersecurity and forensicsâ Offers both quantitative and qualitative assessments of the topics Includes case studies that will be helpful for the researchersProf. Keshav Kaushik is Assistant Professor in the Department of Systemics, School of Computer Science at the University of Petroleum and Energy Studies, Dehradun, India.Dr. Shubham Tayal is Assistant Professor at SR University, Warangal, India.Dr. Akashdeep Bhardwaj is Professor (Cyber Security & Digital Forensics) at the University of Petroleum & Energy Studies (UPES), Dehradun, India.Dr. Manoj Kumar is Assistant Professor (SG) (SoCS) at the University of Petroleum and Energy Studies, Dehradun, India.Table of Contents1. Detection of Cross-Site Scripting and Phishing Website Vulnerabilities Using Machine Learning. 2. A Review: Security and Privacy Defensive Techniques for Cyber Security Using Deep Neural Networks (DNNs). 3. DNA-Based Cryptosystem for Connected Objects and IoT Security. 4. A Role of Digital Evidence: Mobile Forensics Data. 5. Analysis of Kernel Vulnerabilities Using Machine Learning. 6. Cyber Threat Exploitation and Growth during COVID-19 Times. 7. An Overview of the Cybersecurity in Smart Cities in the Modern Digital Age. 8. The Fundamentals and Potential for Cyber Security of Machine Learning in the Modern World. 9. Qualitative and Quantitative Evaluation of Encryption Algorithms. 10. Analysis and Investigation of Advanced Malware Forensics. 11. Network Intrusion Detection System Using Naïve Bayes Classification Technique for Anomaly Detection. 12. Data Security Analysis in Mobile Cloud Computing for Cyber Security. 13. A Comprehensive Review of Investigations of Suspects of Cyber Crimes. 14. Fault Analysis Techniques in Lightweight Ciphers for IoT Devices.

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Book SynopsisThis book endeavours to highlight the untapped potential of Smart Agriculture for the innovation and expansion of the agriculture sector. The sector shall make incremental progress as it learns from associations between data over time through Artificial Intelligence, deep learning and Internet of Things applications. The farming industry and Smart agriculture develop from the stringent limits imposed by a farm's location, which in turn has a series of related effects with respect to supply chain management, food availability, biodiversity, farmers' decision-making and insurance, and environmental concerns among others. All of the above-mentioned aspects will derive substantial benefits from the implementation of a data-driven approach under the condition that the systems, tools and techniques to be used have been designed to handle the volume and variety of the data to be gathered. Contributions to this book have been solicited with the goal of uncovering the possibilities of engaging Table of ContentsMachine learning and deep learning in agriculture, Descriptive and predictive analytics of agricultural data using machine learning algorithms, Discrimination between weed and crop via image analysis using machine learning algorithm, Bio-inspired optimization algorithms for machine learning in agriculture applications, Agricultural modernization with forecasting stages and machine learning, Classification of segmented image using increased global contrast for Paddy plant disease, IOT in agriculture: Survey on technology, challenges and future scope, Role of IoT in sustainable farming, Smart farming: Crop models and decision support systems using IOT, Smart irrigation in farming using internet of things, Automation systems in agriculture via IOT, A complete automated solution for farm field and garden nurture using internet of things, Machine intelligence techniques for agricultural production: Case study with tomato leaf disease detection, Clock signal and its attribute for agriculture.

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Book SynopsisExplanatory Model Analysis Explore, Explain and Examine Predictive Models is a set of methods and tools designed to build better predictive models and to monitor their behaviour in a changing environment. Today, the true bottleneck in predictive modelling is neither the lack of data, nor the lack of computational power, nor inadequate algorithms, nor the lack of flexible models. It is the lack of tools for model exploration (extraction of relationships learned by the model), model explanation (understanding the key factors influencing model decisions) and model examination (identification of model weaknesses and evaluation of model''s performance). This book presents a collection of model agnostic methods that may be used for any black-box model together with real-world applications to classification and regression problems.Trade Review"The structure is well-conceived, with chapters consisting in five sections: intuition, method, example, pros and cons, and code snippets. I sense a teacher’s long experience behind these choices.The chapters contain good mathematical detail on the techniques discussed, but the theory is well balanced with examples and code.The visualizations are great. Often, the gist of a particular technique, and it’s practical, interpretive value, can be gleaned from the visualizations threading through the chapter, along with captions. The authors did a really nice job with this.The rationale for the book is well-described.The discussion of techniques seems both comprehensive (given my sense of the field) and helpfully specific, both at the instance and the dataset levels."-Jeff Webb, University of Utah"The authors are doing a very good job in addressing the potential readers, by providing a clean presentation and practical guidance on diagnostic graphical tools…Having an ‘intuition section’ at the beginning of each chapter is very useful."-Riccardo De Bin, University of Oslo"The book provides a unified presentation of model exploration, visualization, comparison and diagnostics of different machine learning algorithms…This book would be found useful by both students as well as practitioners who analyze their own data. Books including real data examples in R and in Python are needed in this area. (It) will serve as a reference, especially for analyses done with dalex or archivist R package (and )can serve as a textbook of data science courses in many fields including computer science, social sciences, economics and other."-Patricia Martinkova, Institute of Computer Science of the Czech Academy of Sciences"There are books that focus on prediction models, for example the element of statistical learning and an introduction to statistical learning but these are not focused on the evaluation of predictive models which is the main focus on the proposed book and its main advantage. As predictive models become very popular in the last years, such a book that focus on the evaluation of the models and model diagnostics can be very popular."-Ziv Shkedy, Data Science Institute, Hasselt University, Belgium'The book is clearly and consistently structured and well–written. The graphics are explained conceptually and mathematically. There are chapter sections on the pros and cons of what is proposed, where the authors are generally properly cautious and recommend a mixture of approaches.'- Antony Unwin, International Statistical Review, 2021 Volume 89, Issue 3"The structure is well-conceived, with chapters consisting in five sections: intuition, method, example, pros and cons, and code snippets. I sense a teacher’s long experience behind these choices. The chapters contain good mathematical detail on the techniques discussed, but the theory is well balanced with examples and code. The visualizations are great. Often, the gist of a particular technique, and it’s practical, interpretive value, can be gleaned from the visualizations threading through the chapter, along with captions. The authors did a really nice job with this. The rationale for the book is well-described. The discussion of techniques seems both comprehensive (given my sense of the field) and helpfully specific, both at the instance and the dataset levels." -Jeff Webb, University of Utah"The authors are doing a very good job in addressing the potential readers, by providing a clean presentation and practical guidance on diagnostic graphical tools…Having an ‘intuition section’ at the beginning of each chapter is very useful." -Riccardo De Bin, University of Oslo"The book provides a unified presentation of model exploration, visualization, comparison and diagnostics of different machine learning algorithms…This book would be found useful by both students as well as practitioners who analyze their own data. Books including real data examples in R and in Python are needed in this area. (It) will serve as a reference, especially for analyses done with dalex or archivist R package (and )can serve as a textbook of data science courses in many fields including computer science, social sciences, economics and other." -Patricia Martinkova, Institute of Computer Science of the Czech Academy of Sciences"There are books that focus on prediction models, for example the element of statistical learning and an introduction to statistical learning but these are not focused on the evaluation of predictive models which is the main focus on the proposed book and its main advantage. As predictive models become very popular in the last years, such a book that focus on the evaluation of the models and model diagnostics can be very popular." -Ziv Shkedy, Data Science Institute, Hasselt University, Belgium"We need to explore the models and learn about their behaviour. This book presents, explains, and summarises the techniques for doing so. Moreover, it provides code in R and Python for doing so. The methods have many similarities with those of sensitivity analysis developed within the Sensitivity Analysis of Model Output (SAMO) community. ... [M]any doctoral students, professional statisticians and researchers should ensure that they have access to it and know how to use its methods when dealing with highly complex functions in their data and model analysis." -Simon French, in the Journal of the Royal Statistics Society, Series A, June 2022"The book presents a valuable collection of methods for models’ exploration and diagnostics for various machine learning algorithms. It can be useful in the data and computer science courses for students and instructors, as well as for researchers and practitioners who need to analyze and interpret their statistical and machine learning models both of glass-box and blackbox kind. The book also serves as a great primary for applications of the R and Python software and their packages/libraries, so it is valuable in solving various problems of statistical prediction in various fields."-Stan Lipovetsky, in Technometrics, July 2022Table of ContentsI. Introduction 1. Introduction. 2. Model Development. 3. Do-it-yourself. 4. Datasets and models. II. Instance Level. 5. Introduction to Instance-level Exploration. 6. Break-down Plots for Additive Attributions. 7. Break-down Plots for Interactions. 8. Shapley Additive Explanations (SHAP) for Average Attributions. 9. Local Interpretable Model-agnostic Explanations (LIME). 10. Ceteris-paribus Profiles. 11. Ceteris-paribus Oscillations. 12. Local-diagnostics Plots. 13. Summary of Instance-level Exploration. III. Dataset Level. 14. Introduction to Dataset-level Exploration. 15. Model-performance Measures. 16. Variable-importance Measures. 17. Partial-dependence Profiles. 18. Local-dependence and Accumulated-dependence Profiles. 19. Residual Diagnostics Plots. 20. Summary of Model-level Exploration. IV. Use-cases. 21. FIFA 19.

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Book SynopsisThe significant amount of information available in any field requires a systematic and analytical approach to select the most critical information and anticipate major events. During the last decade, the world has witnessed a rapid expansion of applications of artificial intelligence (AI) and machine learning (ML) algorithms to an increasingly broad range of financial markets and problems. Machine learning and AI algorithms facilitate this process understanding, modelling and forecasting the behaviour of the most relevant financial variables. The main contribution of this book is the presentation of new theoretical and applied AI perspectives to find solutions to unsolved finance questions. This volume proposes an optimal model for the volatility smile, for modelling high-frequency liquidity demand and supply and for the simulation of market microstructure features. Other new AI developments explored in this book includes building a universal model for a large number of stockTable of ContentsForewordMarcos Lopez de PradoIntroductionGermán G. Creamer, Gary Kazantsev and Tomaso Aste1. Universal features of price formation in financial markets: perspectives from deep learningJustin Sirignano and Rama Cont2. Far from the madding crowd: collective wisdom in prediction marketsGiulio Bottazzi and Daniele Giachini3. Forecasting limit order book liquidity supply–demand curves with functional autoregressive dynamicsYing Chen, Wee Song Chua and Wolfgang Karl Härdle4. Forecasting market statesPier Francesco Procacci and Tomaso Aste5. Encoding of high-frequency order information and prediction of short-term stock price by deep learningDaigo Tashiro, Hiroyasu Matsushima, Kiyoshi Izumi and Hiroki Sakaji6. Attention mechanism in the prediction of stock price movement by using LSTM: Evidence from the Hong Kong stock market Shun Chen and Lei Ge7. Learning multi-market microstructure from order book dataGeonhwan Ju, Kyoung-Kuk Kim and Dong-Young Lim8. A non-linear causality test: a machine learning approach for energy futures forecastGermán G. Creamer and Chihoon Lee9. The QLBS Q-Learner goes NuQLear: fitted Q iteration, inverse RL, and option portfoliosIgor Halperin10. Detection of false investment strategies using unsupervised learning methodsMarcos López de Prado and Michael J. Lewis

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Book SynopsisOver the last two decades, fieldbus has totally revolutionized the way communication takes place in the fields of process control, automation, and manufacturing industries. Recent introduction of real-time fieldbuses has opened up its application in multi-axis motor control and other time-critical applications. Fieldbus is designed to ensure easy interoperability, smarter network designs, increased data availability, and lessened stress on the design aspects of safety protocols.This second edition of Fieldbus and Networking in Process Automation discusses the different facets of fieldbus technology including design, wiring, installation, and commissioning as well as safety aspects in hostile application areas. The book:â Explains basic communication principles and networkingâa must for understanding fieldbusesâ Considers the advantages and shortcomings of individual fieldbusesâ Provides a broad spectrum of different fieldbuses used in both pTable of ContentsChapter 1 Data Communication..............................................................1Chapter 2 Networking...........................................................................25Chapter 3 Network Models...................................................................35Chapter 4 Networks in Process Automation.........................................53Chapter 5 Fieldbuses.............................................................................63Chapter 6 Highway Addressable Remote Transducer (HART)................ 75Chapter 7 Foundation Fieldbus.............................................................89Chapter 8 PROFIBUS......................................................................... 119Chapter 9 Modbus and Modbus Plus.................................................. 145Chapter 10 CAN Bus............................................................................ 157Chapter 11 DeviceNet........................................................................... 163Chapter 12 AS-i.................................................................................... 169Chapter 13 Seriplex............................................................................... 175Chapter 14 Interbus-S........................................................................... 179Chapter 15 ControlNet.......................................................................... 189Chapter 16 Common Industrial Protocol.............................................. 201Chapter 17 Ethernet and Ethernet/IP.................................................... 213Chapter 18 EtherCAT...........................................................................225Chapter 19 Sercos III............................................................................237Chapter 20 Ethernet Powerlink............................................................. 251Chapter 21 Profinet IRT........................................................................257Chapter 22 Intrinsically Safe Fieldbus Systems....................................265Chapter 23 Wiring, Installation, and Commissioning..........................277Chapter 24 Wireless Sensor Networks..................................................303Chapter 25 WirelessHART (WHART)................................................ 317Chapter 26 ISA100.11a..........................................................................343

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Book SynopsisThis book provides a comprehensive, conceptual, and detailed overview of the wide range of applications of Artificial Intelligence, Machine Learning, and Data Science and how these technologies have an impact on various domains such as healthcare, business, industry, security, and how all countries around the world are feeling this impact. The book aims at low-cost solutions which could be implemented even in developing countries. It highlights the significant impact these technologies have on various industries and on us as humans. It provides a virtual picture of forthcoming better human life shadowed by the new technologies and their applications and discusses the impact Data Science has on business applications. The book will also include an overview of the different AI applications and their correlation between each other. The audience is graduate and postgraduate students, researchers, academicians, institutions, and professionals who are interested in exploring Table of ContentsPart 1: Healthcare. 1. Deep Learning Approaches for Better Medical Imaging. 2. Use of Artificial Intelligence (AI) in Healthcare. 3. Computer Vision and Biomedical Imaging. 4. Disease Diagnosis and Prediction Using Machine Learning. 5. AI and ML Based Mathematical Models for Better Healthcare. Part 2: Human and Society. 6. Internet of Things (IoT) Applications for Safety and Security. 7. Use of AI and Data Science Tools for Fraud and Risk Detection. 8. Probabilistic Predictive Mathematical Tools for Data Science. 9. Natural Language Processing for Human Assistant Systems. 10. Energy Efficient Green Cities. 11. Futuristic Networks. Part 3: Industry and Business Applications. 12. Technology Driven Strategic Business Plans. 13. Data Science Applications for Decision-Making. 14. IOT Based Recommender Systems. 15. Optimization Techniques for Strategic Decision-Making. 16. Technology for Robust Cyber Security.

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Book SynopsisHuman-Robot Interaction: Safety, Standardization, and Benchmarking provides a comprehensive introduction to the new scenarios emerging where humans and robots interact in various environments and applications on a daily basis. The focus is on the current status and foreseeable implications of robot safety, approaching these issues from the standardization and benchmarking perspectives. Featuring contributions from leading experts, the book presents state-of-the-art research, and includes real-world applications and use cases. It explores the key leading sectorsârobotics, service robotics, and medical roboticsâand elaborates on the safety approaches that are being developed for effective human-robot interaction, including physical robot-human contacts, collaboration in task execution, workspace sharing, human-aware motion planning, and exploring the landscape of relevant standards and guidelines.Features Presenting aTable of Contents 1 The Role of Standardization in Technical Regulations André Pirlet 2 The intricate relationships between private standards and publicpolicymakingin the case of personal care robot. Who cares more? Eduard Fosch-Villaronga and Angelo Jr Golia 3 Standard Ontologies and HRI Sandro Rama Fiorini, Abdelghani Chibani, Tamas Haidegger, Joel Luis Carbonera, Craig Schlenoff, Jacek Malec, Edson Prestes, Paulo Gonçalves, S. Veera Ragavan, Howard Li, Hirenkumar Nakawala, Stephen Balakirsky, Sofiane Bouznad, Noauel Ayari, and Yacine Amirat 4 Robot Modularity and safety for Service Robots Hong Seong Park and Gurvinder Singh Virk 5 Human-robot shared workspace in aerospace factories Gilber Tang 6 Workspace sharing in mobile manipulation José Saenz 7 On rehabilitation robotics safety, benchmarking, standards Jan F. Veneman 8 A practical appraisal of ISO 13482 as a reference for an orphan robot category Paolo Barattini 9 Safety of Medical Robots, Regulation and Standards Kiyo Chinzei 10 The Other End of Human–Robot Interaction: Models for Safe and Efficient Tool–Tissue Interactions Arpad Takacs, Imre J. Rudas, Tamas Haidegger 11 Passive Bilateral Teleoperation with Safety Considerations Lorinc Marton 12 Human-Robot Interfaces in Autonomous Surgical Robots Paolo Fiorini and Riccardo Muradore

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Book SynopsisThe great majority of books on artificial intelligence are written by AI experts who understandably focus on its achievements and potential transformative effects on society. In contrast, AI vs Humans is written by two psychologists (Michael and Christine Eysenck) whose perspective on AI (including robotics) is based on their knowledge and understanding of human cognition. This book evaluates the strengths and limitations of people and AI. The authors' expertise equips them well to consider this by seeing how well (or badly) AI compares to human intelligence. They accept that AI matches or exceeds human ability in many spheres such as mathematical calculations, complex games (e.g., chess, Go, and poker), diagnosis from medical images, and robotic surgery.However, the human tendency to anthropomorphise has led many people to claim mistakenly that AI systems can think, infer, reason, and understand while engaging in information processing. In fact, such systems lack aTable of Contents1. Brief History of AI and Robotics 2. AI Dominance 3. Human Strengths 4. How (Un)Intelligent is AI? 5. Human Limitations 6. Robots and Morality 7. And the Winner is? 8. The Future

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Book SynopsisThis Open Access book brings the experiences of automation as part of quotidian life into focus. It asks how, where and when automated technologies and systems are emerging in everyday life across different global regions? What are their likely impacts in the present and future? How do engineers, policy makers, industry stakeholders and designers envisage artificial intelligence (AI) and automated decision-making (ADM) as solutions to individual and societal problems? How do these future visions compare with the everyday realities, power relations and social inequalities in which AI and ADM are experienced? What do people know about automation and what are their experiences of engaging with actually existing' AI and ADM technologies? An international team of leading scholars bring together research developed across anthropology, sociology, media and communication studies and ethnology, which shows how by rehumanising automation, we can gain deeper understandings of its societalTable of ContentsIntroduction: Everyday Automation: setting a research agenda PART I: Challenging dominant narratives of automation 1. Imagining Mundane Automation: Historical Trajectories of Meaning Making around Technological Change 2. Trust, Ethics and Automation: Anticipatory Imaginaries in Everyday Life 3. The Quantified Pandemic: Digitised Surveillance, Containment and Care in Response to the COVID-19 Crisis 4. Less Work for Teacher? The Ironies of Automated Decision-making in Schools PART II Embedding automated systems in the everyday 5. Alexa’s Got a Hunch: The Human Decisions behind Programming Emotion-sensing and Caregiving into Digital Assistants 6. Framing Fashion: Human-Machine Learning and the Amazon Echo Look 7. Coffee with the Algorithm: Imaginaries, Maintenance and Care in the Everyday Life of a News-ranking Algorithm 8. Everyday AI at Work: Self-tracking and Automated Communication for Smart Work 9. Exploring ADM in Clinical Decision-Making: Healthcare Experts Encountering Digital Automation PART III Experimenting with Automation in Society 10. Hate it? Automate it!: Thinking and Doing Robotic Process Automation and Beyond 11. Smart Thermostats and the Algorithmic Control of Thermal Comfort 12. Prisoners Training AI: Ghosts, Humans and Values in Data Labour 13. Investigating ADM in Shared Mobility: A Design Ethnographic Approach 14. Ad Accountability Online: A Methodological Approach.

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Book SynopsisVectors and Tensors.- The Geometry of Deformation.- The Transmission of Force.- Elastic Constitutive Theory.- Boundary Value Problems in Elasticity.- The Ritz Method of Approximation.- The Linear Theory of Beams.- The Linear Theory of Plates.- Energy Principles and Static Stability.- Fundamental Concepts in Static Stability.- The Planar Buckling of Beams.- Numerical Computation for Nonlinear Problems.Table of ContentsVectors and Tensors.- The Geometry of Deformation.- The Transmission of Force.- Elastic Constitutive Theory.- Boundary Value Problems in Elasticity.- The Ritz Method of Approximation.- The Linear Theory of Beams.- The Linear Theory of Plates.- Energy Principles and Static Stability.- Fundamental Concepts in Static Stability.- The Planar Buckling of Beams.- Numerical Computation for Nonlinear Problems.

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Book SynopsisStructured in two parts, the first section focuses on probabilistic graphical models, while the second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision process and partially ordered decision problems.Trade ReviewFrom the reviews:MATHEMATICAL REVIEWS"This is indeed an invaluable text for students in information technology, engineering, and statistics. It is also very helpful for researchers in these fields and for those working in industry. The book is self-contained…The book has enough illustrative examples and exercises for the reader. All the illustrations are motivated by real applications. Moreover, the book provides a good balance between pure mathematical treatment and the applied aspects of the subject.""The Bayesian network (BN), or probabilistic expert system, is technology for automating human-life reasoning under uncertainty in specific contexts. … the book does an admirable job of concisely explaining a great range of concepts and techniques. … the book is very well written and to my knowledge nothing else meets its specific goal of quickly equipping the reader with both practical skills and sufficient theoretical background. … I certainly would not want to try to implement a BN application without reading this book.” (David Tritchler, Sankhya: Indian Journal of Statistics, Vol. 64 (B Part 3), 2002)"Professor Jensen is certainly one of the most influential researchers in the field of Bayesian networks and it is not surprising that this book represents a very clear and useful presentation of the main properties and use of graphical models. … I think that the present volume represents a useful integration of other material and a compact guide for either a student who wants an introduction to the field or a teacher who needs a reference for a course on probabilistic reasoning in AI." (Luigi Portinale, The Computer Journal, Vol. 46 (3), 2003)"This book is an introduction to Bayesian networks at an accessible level for first-year graduate or advanced undergraduate students. … I found this book to be an excellent introduction to the topic. It is well written, provides broad topic coverage, and is quite accessible to the non-expert. … I think Bayesian Networks and Decision Graphs would make a fine text for an introductory class in Bayesian networks or a useful reference for anyone interested in learning about the field." (David J. Marchette, Technometrics, Vol. 45 (2), 2003)"I can comfortably recommend this book as a primary source for topics related to Bayesian networks and decision graphs. This would be an excellent edition to any personal library." (Technometrics, Feburary 2008)From the reviews of the second edition:"The present book provides a very readable but also rigorous and comprehensive introduction to the subject. It would make a very good text for a graduate or an advanced undergraduate course. … Altogether, this is a very useful book for anyone interested in learning Bayesian networks without tears." (Jayanta K. Ghosh, International Statistical Reviews, Vol. 76 (2), 2008)"This book is the second edition of Jensen’s Bayesian Networks and Decision Graphs … . Each chapter ends with a summary section, bibliographic notes, and exercises. … provides a readable, self-contained, and above all, practical introduction to Bayesian networks and decision graphs. Its treatment is appropriate not just for statisticians, but also for computer scientists, engineers, and others researchers with appropriate mathematical background. … highly recommend it as a text or a useful reference for anyone interested in probabilistic graphical models or decision graphs." (Alyson G. Wilson, Journal of the American Statistical Association, Vol. 104 (485), March, 2009)“Devoted to Bayesian Networks or Graphical Models and Influence Diagrams, covering a full course with nice exercises … . It is useful as a reference for special topics. … strongly recommended for readers or user of BNs who are interested in specifying dependency models. … great importance to practitioners who try to find causality behind call-backs of products or crashes. … the book can be recommended to anybody working on the interface of operations research, AI, statistics and computer science.”­­­ (Hans-J. Lenz, Statistical Papers, Vol. 52, 2011)Table of ContentsCausal and Bayesian Networks * Part I: A Practical Guide to Normative Systems: Building Models * Learning, Adaptation, and Tuning * Decision Graphs * Part II: Algorithms for Normative Systems: Belief Updating in Bayesian Networks * Bayesian Network Analysis Tools * Algorithms for Influence Diagrams

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Book SynopsisIt can be used for a course that provides an introduction to basic functional analysis, approximation theory, and numerical analysis, while building upon and applying basic techniques of real variable theory.Trade ReviewSecond Edition S.C. Brenner and L.R. Scott The Mathematical Theory of Finite Element Methods "[This is] a well-written book. A great deal of material is covered, and students who have taken the trouble to master at least some of the advanced material in the later chapters would be well placed to embark on research in the area." ZENTRALBLATT MATH From the reviews of the third edition: "An excelent survey of the deep mathematical roots of finite element methods as well as of some of the newest and most formal results concerning these methods. … The approach remains very clear and precise … . A significant number of examples and exercises improve considerably the accessability of the text. The authors also point out different ways the book could be used in various courses. … valuable reference and source for researchers (mainly mathematicians) in the topic." (Calin Ioan Gheorghiu, Zentralblatt MATH, Vol. 1135 (13), 2008)Table of ContentsPreface(3rdEd).- Preface(2ndEd).- Preface(1stED).- Basic Concepts.- Sobolev Spaces.- Variational Formulation of Elliptic Boundary Value Problems.- The Construction of a Finite Element of Space.- Polynomial Approximation Theory in Sobolev Spaces.- n-Dimensional Variational Problems.- Finite Element Multigrid Methods.- Additive Schwarz Preconditioners.- Max-norm Estimates.- Adaptive Meshes.- Variational Crimes.- Applications to Planar Elasticity.- Mixed Methods.- Iterative Techniques for Mixed Methods.- Applications of Operator-Interpolation Theory.- References.- Index.

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Book Synopsis1 Sphere Packings and Kissing Numbers.- 2 Coverings, Lattices and Quantizers.- 3 Codes, Designs and Groups.- 4 Certain Important Lattices and Their Properties.- 5 Sphere Packing and Error-Correcting Codes.- 6 Laminated Lattices.- 7 Further Connections Between Codes and Lattices.- 8 Algebraic Constructions for Lattices.- 9 Bounds for Codes and Sphere Packings.- 10 Three Lectures on Exceptional Groups.- 11 The Golay Codes and the Mathieu Groups.- 12 A Characterization of the Leech Lattice.- 13 Bounds on Kissing Numbers.- 14 Uniqueness of Certain Spherical Codes.- 15 On the Classification of Integral Quadratic Forms.- 16 Enumeration of Unimodular Lattices.- 17 The 24-Dimensional Odd Unimodular Lattices.- 18 Even Unimodular 24-Dimensional Lattices.- 19 Enumeration of Extremal Self-Dual Lattices.- 20 Finding the Closest Lattice Point.- 21 Voronoi Cells of Lattices and Quantization Errors.- 22 A Bound for the Covering Radius of the Leech Lattice.- 23 The Covering Radius of the Leech Lattice.- 24 Twenty-Three Constructions for the Leech Lattice.- 25 The Cellular Structure of the Leech Lattice.- 26 Lorentzian Forms for the Leech Lattice.- 27 The Automorphism Group of the 26-Dimensional Even Unimodular Lorentzian Lattice.- 28 Leech Roots and Vinberg Groups.- 29 The Monster Group and its 196884-Dimensional Space.- 30 A Monster Lie Algebra?.- Supplementary Bibliography.Trade ReviewThird Edition J.H. Conway and N.J.A. Sloane Sphere Packings, Lattices and Groups "This is the third edition of this reference work in the literature on sphere packings and related subjects. In addition to the content of the preceding editions, the present edition provides in its preface a detailed survey on recent developments in the field, and an exhaustive supplementary bibliography for 1988-1998. A few chapters in the main text have also been revised."—MATHEMATICAL REVIEWSTable of Contents1 Sphere Packings and Kissing Numbers.- 2 Coverings, Lattices and Quantizers.- 3 Codes, Designs and Groups.- 4 Certain Important Lattices and Their Properties.- 5 Sphere Packing and Error-Correcting Codes.- 6 Laminated Lattices.- 7 Further Connections Between Codes and Lattices.- 8 Algebraic Constructions for Lattices.- 9 Bounds for Codes and Sphere Packings.- 10 Three Lectures on Exceptional Groups.- 11 The Golay Codes and the Mathieu Groups.- 12 A Characterization of the Leech Lattice.- 13 Bounds on Kissing Numbers.- 14 Uniqueness of Certain Spherical Codes.- 15 On the Classification of Integral Quadratic Forms.- 16 Enumeration of Unimodular Lattices.- 17 The 24-Dimensional Odd Unimodular Lattices.- 18 Even Unimodular 24-Dimensional Lattices.- 19 Enumeration of Extremal Self-Dual Lattices.- 20 Finding the Closest Lattice Point.- 21 Voronoi Cells of Lattices and Quantization Errors.- 22 A Bound for the Covering Radius of the Leech Lattice.- 23 The Covering Radius of the Leech Lattice.- 24 Twenty-Three Constructions for the Leech Lattice.- 25 The Cellular Structure of the Leech Lattice.- 26 Lorentzian Forms for the Leech Lattice.- 27 The Automorphism Group of the 26-Dimensional Even Unimodular Lorentzian Lattice.- 28 Leech Roots and Vinberg Groups.- 29 The Monster Group and its 196884-Dimensional Space.- 30 A Monster Lie Algebra?.- Supplementary Bibliography.

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Book SynopsisAn NPR 2023 "Books We Love" Pick One of the Next Big Idea Club's Must-Read Books An award-winning defense expert tells the story of today’s great power rivalry—the struggle to control artificial intelligence.Trade Review"Should be required reading for anyone interested in the future of the global economy or geopolitics." -- Thomas E. Ricks - New York Times Book Review"Scharre is a thoughtful, knowledgeable, and capable guide. He explains why AI matters and charts the areas that will determine which country gets the most out of its investments." -- Lawrence Freedman - Foreign Affairs"An invaluable primer to arguably the most important driver of change for our future. Scharre marshals fact after fact to explain not just the technology, but the trends soon to unfold and remake our world." -- P. W. Singer, author of Burn-In"Paul Scharre argues that the AI race between democratic and authoritarian states is well underway, and the stakes could not be higher: whoever wins will write the international rules of the next century. With revealing anecdotes, cogent analysis, and incisive insight, Scharre demystifies AI and its national security implications. If you read one book on AI this year, read this one!" -- Michèle Flournoy, former Under Secretary of Defense for Policy"How will AI change the balance of power between authoritarian states and democracies? This is one of the most important questions in geopolitics today. Authoritarians have already figured out how to use AI to their maximum advantage, and democrats must urgently do the same or risk losing the contest. First step: Read this book, a farsighted and comprehensive survey of the issues involved and the paths forward." -- Pedro Domingos, author of The Master Algorithm"America and its military are facing a major test when it comes to AI. The country that best incorporates artificial intelligence technology into its defense will have significant military advantages over its competitors. Four Battlegrounds is an essential book for everyone involved in American leadership and American defense, because it outlines the challenges we face and explains the key components that will determine our success in using this important new technology to support American power and American ideals." -- Admiral James Stavridis, 16th Supreme Allied Commander of NATO"In this riveting book on AI and power by one of the leading strategists of our time, Paul Scharre highlights an existential challenge: as Americans and Chinese militarize ever more powerful AI to avoid ceding control to each other, they risk ceding too much power to machines." -- Prof. Max Tegmark, MIT AI researcher and author of Life 3.0"A must-read guide to how the emerging artificial intelligence arms race will shape the geopolitical, economic, and political struggle between China and its authoritarian allies and the democratic West led by the United States and Europe." -- Martin Ford, author of Rule of the Robots and Rise of the Robots"A solid, well-organized account of the military applications of AI and of the race to take the lead global position." -- Kirkus Reviews"Technophiles and technophobes alike will be challenged and enlightened." -- Publishers Weekly"Readers knowledgeable about computer science will find it clarifying, while other will gain immense understand of an often opaque if important subject." -- James Pekoll - Booklist

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Book SynopsisAlgorithms will soon know more about us than we know ourselves Where should machine automation end? Is it acceptable to have a digital assistant arrange your calendar, but not to have a robot spouse? Are companion robots acceptable for seniors in need of comfort, but not okay for toddlers exposed to emotional software that could influence their behavior? Is it desirable to live a life within the virtual reality of Facebook’s Oculus Rift, but not if your thoughts are sold to advertisers who manipulate your purchases? We’ve entered an era where a myriad of personalization algorithms influence our every decision, and the lines between human assistance, automation, and extinction have blurred.  We need to create ethical standards for the Artificial Intelligence usurping our lives, and allow individuals to control their identity based on their values. Otherwise, we sacrifice our humanity for productivity versus purpose and for profits versus people.&

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Book SynopsisThis book is intended to be used as a textbook on senior/graduate level courses in human factors engineering and ergonomics.Trade Review`This book contains a lot of useful data and will be of interest not only to the professionals but also to students of ergonomics.' Aslib Table of ContentsAn introduction to ergonomics. Skeletal system. Muscular system and work. Engineering anthropometry. Biomechanical bases of ergonomics. Work physiology. Workstation design. Cumulative trauma disorders. Manual materials handling. Work-tool design. Human-machine systems. Thermal environments. Light and vision. Occupational noise environment. Occupational vibration. Shift work. Office ergonomics. Ergonomics assessment of the workplace. Implementation of ergonomics programs. Appendices. Index.

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Book SynopsisThe eagerly anticipated updated resource on one of the most important areas of research and development: multi-agent systems Multi-agent systems allow many intelligent agents to interact with each other, and this field of study has advanced at a rapid pace since the publication of the first edition of this book, which was nearly a decade ago.Trade Review“Nevertheless, despite these minor issues, this book is highly recommended to all socio-economic agent-based modellers, beginners or otherwise. Wooldridge’s scope, rigor, and well-respected experience at the current coalface means there’s plenty in here of interest for old-timers, while beginners can skip some of the maths and more bleeding-edge theory and concentrate easily on the implementation without loosing much.” (Appl. Spatial Analysis, 2011) Table of ContentsPreface xiii Acknowledgements xxi Part I Setting the Scene 1 1 Introduction 3 1.1 The Vision Thing 6 1.2 Some Views of the Field 9 1.2.1 Agents as a paradigm for software engineering 9 1.2.2 Agents as a tool for understanding human societies 12 1.3 Frequently Asked Questions (FAQ) 12 Part II Intelligent Autonomous Agents 19 2 Intelligent Agents 21 2.1 Intelligent Agents 26 2.2 Agents and Objects 28 2.3 Agents and Expert Systems 30 2.4 Agents as Intentional Systems 31 2.5 Abstract Architectures for Intelligent Agents 34 2.6 How to Tell an Agent What to Do 38 3 Deductive Reasoning Agents 49 3.1 Agents as Theorem Provers 50 3.2 Agent-Oriented Programming 55 3.3 Concurrent MetateM 56 4 Practical Reasoning Agents 65 4.1 Practical Reasoning = Deliberation +Means–Ends Reasoning 65 4.2 Means–Ends Reasoning 69 4.3 Implementing a Practical Reasoning Agent 75 4.4 The Procedural Reasoning System 79 5 Reactive and Hybrid Agents 85 5.1 Reactive Agents 85 5.1.1 The subsumption architecture 86 5.1.2 PENGI 90 5.1.3 Situated automata 90 5.1.4 The agent network architecture 91 5.1.5 The limitations of reactive agents 92 5.2 Hybrid Agents 92 5.2.1 Touring Machines 94 5.2.2 InteRRaP 96 5.2.3 3T 98 5.2.4 Stanley 99 Part III Communication and Cooperation 105 6 Understanding Each Other 107 6.1 Ontology Fundamentals 108 6.1.1 Ontology building blocks 108 6.1.2 Anontology of ontologies 110 6.2 Ontology Languages 113 6.2.1 XML–adhoc ontologies 113 6.2.2 OWL–The web ontology language 114 6.2.3 KIF–ontologies in first-order logic 120 6.3 RDF 121 6.4 Constructing an Ontology 124 6.5 Software Tools for Ontologies 127 7 Communicating 131 7.1 Speech Acts 132 7.1.1 Austin 132 7.1.2 Searle 133 7.1.3 The plan-based theory of speech acts 134 7.1.4 Speech acts as rational action 135 7.2 Agent Communication Languages 136 7.2.1 KQML 136 7.2.2 The FIPA agent communication language 140 7.2.3 JADE 146 8 Working Together 151 8.1 Cooperative Distributed Problem Solving 151 8.2 Task Sharing and Result Sharing 153 8.2.1 Task sharing in the Contract Net 156 8.3 Result Sharing 159 8.4 Combining Task and Result Sharing 159 8.5 Handling Inconsistency 161 8.6 Coordination 162 8.6.1 Coordination through partial global planning 163 8.6.2 Coordination through joint intentions 165 8.6.3 Coordination by mutual modelling 170 8.6.4 Coordination by norms and social laws 173 8.7 Multiagent Planning and Synchronization 177 9 Methodologies 183 9.1 When is an Agent-Based Solution Appropriate? 183 9.2 Agent-Oriented Analysis and Design 184 9.2.1 The AAII methodology 184 9.2.2 Gaia 186 9.2.3 Tropos 187 9.2.4 Prometheus 188 9.2.5 Agent UML 188 9.2.6 Agents in Z 189 9.3 Pitfalls of Agent Development 190 9.4 Mobile Agents 193 10 Applications 201 10.1 Agents for Workflow and Business Process Management 201 10.2 Agents for Distributed Sensing 203 10.3 Agents for Information Retrieval and Management 205 10.4 Agents for Electronic Commerce 211 10.5 Agents for Human–Computer Interfaces 213 10.6 Agents for Virtual Environments 214 10.7 Agents for Social Simulation 214 10.8 Agents for X 218 Part IV Multiagent Decision Making 221 11 Multiagent Interactions 223 11.1 Utilities and Preferences 223 11.2 Setting the Scene 226 11.3 Solution Concepts and Solution Properties 229 11.3.1 Dominant strategies 230 11.3.2 Nash equilibria 230 11.3.3 Pareto efficiency 233 11.3.4 Maximizing social welfare 235 11.4 Competitive and Zero-Sum Interactions 235 11.5 The Prisoner’s Dilemma 236 11.5.1 The shadow of the future 240 11.5.2 Program equilibria 243 11.6 Other Symmetric 2 ×2Interactions 245 11.7 Representing Multiagent Scenarios 248 11.8 Dependence Relations in Multiagent Systems 249 12 Making Group Decisions 253 12.1 Social Welfare Functions and Social Choice Functions 253 12.2 Voting Procedures 255 12.2.1 Plurality 255 12.2.2 Sequential majority elections 257 12.2.3 The Borda count 260 12.2.4 The Slater ranking 260 12.3 Desirable Properties for Voting Procedures 261 12.3.1 Arrow’s theorem 263 12.4 Strategic Manipulation 264 13 Forming Coalitions 269 13.1 Cooperative Games 270 13.1.1 The core 272 13.1.2 The Shapley value 274 13.2 Computational and Representational Issues 277 13.3 Modular Representations 278 13.3.1 Induced subgraphs 278 13.3.2 Marginal contribution nets 280 13.4 Representations for Simple Games 281 13.4.1 Weighted voting games 282 13.4.2 Network flow games 285 13.5 Coalitional Games with Goals 287 13.6 Coalition Structure Formation 288 14 Allocating Scarce Resources 293 14.1 Classifying Auctions 294 14.2 Auctions for Single Items 295 14.2.1 English auctions 295 14.2.2 Dutch auctions 296 14.2.3 First-price sealed-bid auctions 296 14.2.4 Vickrey auctions 296 14.2.5 Expected revenue 297 14.2.6 Lies and collusion 298 14.2.7 Counter speculation 299 14.3 Combinatorial Auctions 299 14.3.1 Bidding languages 302 14.3.2 Winner determination 306 14.3.3 The VCG mechanism 308 14.4 Auctions in Practice 310 14.4.1 Online auctions 310 14.4.2 Adwords auctions 311 14.4.3 The trading agent competition 312 15 Bargaining 315 15.1 Negotiation Parameters 315 15.2 Bargaining for Resource Division 317 15.2.1 Patient players 317 15.2.2 Impatient players 320 15.2.3 Negotiation decision functions 321 15.2.4 Applications of alternating offers 323 15.3 Bargaining for Task Allocation 323 15.3.1 Themonotonic concession protocol 326 15.3.2 The Zeuthen strategy 327 15.3.3 Deception 329 15.4 Bargaining for Resource Allocation 330 16 Arguing 337 16.1 Types of Argument 338 16.2 Abstract Argumentation 338 16.2.1 Preferred extensions 339 16.2.2 Credulous and skeptical acceptance 341 16.2.3 Preferences in abstract argument systems 343 16.2.4 Values in abstract argument systems 344 16.3 Deductive Argumentation Systems 345 16.4 Dialogue Systems 348 16.5 Implemented Argumentation Systems 350 17 Logical Foundations 355 17.1 Logics for Knowledge and Belief 355 17.1.1 Possible-worlds semantics for modal logics 357 17.1.2 Normal modal logics 358 17.1.3 Normal modal logics as epistemic logics 361 17.1.4 Logical omniscience 363 17.1.5 Axioms for knowledge and belief 364 17.1.6 Multiagent epistemic logics 365 17.1.7 Common and distributed knowledge 367 17.2 Logics for Mental States 369 17.2.1 Cohen and Levesque’s intention logic 369 17.2.2 Modelling speech acts 371 17.3 Logics for Cooperation 373 17.3.1 Incomplete information 375 17.3.2 Cooperation logics for social choice 376 17.4 Putting Logic to Work 376 17.4.1 Logic in specification 377 17.4.2 Logic in implementation 378 17.4.3 Logic in verification 381 Part V Coda 391 A A History Lesson 393 B Afterword 405 Glossary of Key Terms 407 References 425 Index 453

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Book SynopsisThis book explores social mechanisms that drive network change and link them to computationally sound models of changing structure to detect patterns. This text identifies the social processes generating these networks and how networks have evolved. Reviews: this book is easy to read and entertaining, and much can be learned from it. Even if you know just abouteverything about large-scale and temporal networks, the book is a worthwhile read; you will learn a lot about SNA literature, patents, the US Supreme Court, and European soccer. (Social Networks) a clear and accessible textbook, balancing symbolic maths, code, and visual explanations. The authors' enthusiasm for the subject matter makes it enjoyable to read (JASSS)Table of ContentsPreface xiii 1 Temporal and Spatial Networks 1 1.1 Modern Social Network Analysis 1 1.2 Network Sizes 3 1.3 Substantive Concerns 3 1.3.1 Citation Networks 3 1.3.2 Other Types of Large Networks 7 1.4 Computational Methods 10 1.5 Data for Large Temporal Networks 12 1.5.1 The Main Datasets 12 1.5.2 Secondary Datasets 14 1.6 Induction and Deduction 16 2 Foundations of Methods for Large Networks 18 2.1 Networks 18 2.1.1 Descriptions of Networks 20 2.1.2 Degrees 21 2.1.3 Descriptions of Properties 21 2.1.4 Visualizations of Properties 22 2.2 Types of Networks 22 2.2.1 Temporal Networks 23 2.2.2 Multirelational Networks 25 2.2.3 Two-mode Networks 28 2.3 Large Networks 28 2.3.1 Small and Middle Sized Networks 29 2.3.2 Large Networks 30 2.3.3 Complexity of Algorithms 30 2.4 Strategies for Analyzing Large Networks 32 2.5 Statistical Network Measures 33 2.5.1 Using Pajek and R Together 35 2.5.2 Fitting Distributions 35 2.6 Subnetworks 37 2.6.1 Clusters, Clusterings, Partitions, Hierarchies 37 2.6.2 Contractions of Clusters 38 2.6.3 Subgraphs 40 2.6.4 Cuts 42 2.7 Connectivity Properties of Networks 46 2.7.1 Walks 46 2.7.2 Equivalence Relations and Partitions 47 2.7.3 Connectivity 48 2.7.4 Condensation 49 2.7.5 Bow-tie Structure of the Web Graph 50 2.7.6 The Internal Structure of Strong Components 51 2.7.7 Bi-connectivity and -connectivity 51 2.8 Triangular and Short Cycle Connectivities 53 2.9 Islands 54 2.9.1 Defining Islands 55 2.9.2 Some Properties of Islands 56 2.10 Cores and Generalized Cores 57 2.10.1 Cores 58 2.10.2 Generalized Cores 59 2.11 Important Vertices in Networks 61 2.11.1 Degrees, Closeness, Betweenness and Other Indices 63 2.11.2 Clustering 65 2.11.3 Computing Further Indices Through Functions 66 2.12 Transition to Methods for Large Networks 68 3 Methods for Large Networks 69 3.1 Acyclic Networks 71 3.1.1 Some Basic Properties of Acyclic Networks 71 3.1.2 Compatible Numberings: Depth and Topological Order 72 3.1.3 Topological Orderings and Functions on Acyclic Networks 74 3.2 SPC Weights in Acyclic Networks 75 3.2.1 Citation Networks 75 3.2.2 Analysis of Citation Networks 76 3.2.3 Search Path Count Method 77 3.2.4 Computing SPLC and SPNP Weights 77 3.2.5 Implementation Details 78 3.2.6 Vertex Weights 78 3.2.7 General Properties of Weights 79 3.2.8 SPC Weights 80 3.3 Probabilistic Flow in Acyclic Network 81 3.4 Nonacyclic Citation Networks 82 3.5 Two-mode Networks from Data Tables 84 3.5.1 Multiplication of Two-mode Networks 85 3.6 Bibliographic Networks 88 3.6.1 Co-authorship Networks 88 3.6.2 Collaboration Networks 89 3.6.3 Other Derived Networks 92 3.7 Weights 94 3.7.1 Normalizations of Weights 94 3.7.2 -Rings 94 3.7.3 4-Rings and Analysis of Two-mode Networks 95 3.7.4 Two-mode Cores 96 3.8 Pathfinder 96 3.8.1 Pathfinder Algorithms 100 3.8.2 Computing the Closure Over the Pathfinder Semiring 101 3.8.3 Spanish Algorithms 101 3.8.4 A Sparse Network Algorithm 102 3.9 Clustering, Blockmodeling, and Community Detection 102 3.9.1 The Louvain Method and VOS 102 3.10 Clustering Symbolic Data 103 3.10.1 Symbolic Objects Described with Distributions 103 3.10.2 The Leaders Method 105 3.10.3 An AgglomerativeMethod 107 3.11 Approaches to Temporal Networks 107 3.11.1 Journeys -- Walks in Temporal Networks 108 3.11.2 Measures 110 3.11.3 Problems and Algorithms 111 3.11.4 Evolution 114 3.12 Levels of Analysis 114 3.13 Transition to Substantive Topics 116 4 Scientific Citation and Other Bibliographic Networks 117 4.1 The Centrality Citation Network 117 4.2 Preliminary Data Analyses 118 4.2.1 Temporal Distribution of Publications 119 4.2.2 Degree Distributions of the Centrality Literature 121 4.2.3 Types of Works 124 4.2.4 The Boundary Problem 126 4.3 Transforming a Citation Network into an Acyclic Network 128 4.3.1 Checking for the Presence of Cycles 128 4.3.2 Dealing with Cycles in Citation Networks 133 4.4 The Most ImportantWorks 134 4.5 SPC Weights 134 4.5.1 Obtaining SPC Weights and Drawing Main Paths 135 4.5.2 The Main Path of the Centrality Citation Network 135 4.6 Line Cuts 139 4.7 Line Islands 141 4.7.1 The Main Island 143 4.7.2 A Geophysics and Meteorology Line Island 145 4.7.3 An Optical Network Line Island 150 4.7.4 A Partial Summary of Main Path and Line Island Results 154 4.8 Other Relevant Subnetworks for a Bounded Network 155 4.9 Collaboration Networks 157 4.9.1 Macros for Collaboration Networks 158 4.9.2 An Initial Attempt of Analyses of Collaboration Networks 159 4.10 A Brief Look at the SNA Literature SN5 Networks 160 4.11 On the Centrality and SNA Collaboration Networks 173 References 173 5 Citation Patterns in Temporal United States Patent Data 175 5.1 Patents 175 5.2 Supreme Court Decisions Regarding Patents 179 5.2.1 Co-cited Decisions 179 5.2.2 Citations Between Co-cited Decisions 182 5.3 The 1976--2006 Patent Data 183 5.4 Structural Variables Through Time 184 5.4.1 Temporally Specific Networks 184 5.4.2 Shrinking Specific Patent Citation Networks 186 5.4.3 Structural Properties 187 5.5 Some Patterns of Technological Development 188 5.5.1 Structural Properties of Temporally Specific Networks 190 5.6 Important Subnetworks 193 5.6.1 Line Islands 194 5.6.2 Line Islands with Patents Tagged by Keywords 196 5.6.3 Vertex Islands 201 5.7 Citation Patterns 202 5.7.1 Patents from 1976, Cited Through to 2006 204 5.7.2 Patents from 1987, Cited Through to 2006 209 5.8 Comparing Citation Patterns for Two Time Intervals 211 5.9 Summary and Conclusions 214 6 The US Supreme Court Citation Network 216 6.1 Introduction 217 6.2 Co-cited Islands of Supreme Court Decisions 219 6.3 A Native American Line Island 222 6.3.1 Forced Removal of Native American Populations 222 6.3.2 RegulatingWhites on Native American Lands 224 6.3.3 Curtailing the Authority of Native American Courts 224 6.3.4 Taxing Native Americans and Enforcing External Laws 225 6.3.5 The Presence of Non-Native Americans on Native American Lands 226 6.3.6 Some Later Developments 227 6.3.7 A Partial Summary 227 6.4 A ‘Perceived Threats to Social Order’ Line Island 228 6.4.1 Perceived Threats to Social Order 228 6.4.2 The Structures of the Threats to Social Order Line Island 230 6.4.3 Decisions Involving Communists and Socialists 230 6.4.4 Restrictions of Labor Groups Organizing 236 6.4.5 Restrictions of African Americans Organizing 237 6.4.6 Jehovah’sWitnesses as a Perceived Threat 239 6.4.7 Obscenity as a Threat to Social Order 243 6.5 Other Perceived Threats 246 6.6 The Dred Scott Decision 250 6.6.1 Citations from Dred Scott 251 6.6.2 Citations to Dred Scott 253 6.6.3 Methodological Implications of Dred Scott 260 6.7 Further Reflections on the Supreme Court Citation Network 261 7 Football as the World’s Game 263 7.1 A Brief Historical Overview 264 7.2 Football Clubs 264 7.3 Football Players 266 7.4 Football in England 267 7.5 Player Migrations 268 7.6 Institutional Arrangements and the Organization of Football 269 7.7 Court Rulings 271 7.8 Specific Factors Impacting Football Migration 272 7.9 Some Arguments and Propositions 272 7.10 Some Preliminary Results 278 7.10.1 The Non-English Presence in the EPL 279 7.10.2 Player Fitness 289 7.10.3 Starting Clubs for English Players 292 7.10.4 General Features of the Top Five European Leagues 295 7.10.5 Flows of Footballers into the Top European Leagues 301 7.11 Player Ages When Recruited to the EPL 303 7.12 A Partial Summary of Results 305 8 Networks of Player Movements to the EPL 308 8.1 Success in the EPL 308 8.2 The Overall Presence of Other Countries in the EPL 311 8.3 Network Flows of Footballers Between Clubs to Reach the EPL 312 8.3.1 Moving Directly into the EPL from Local and Non-local Clubs 313 8.3.2 Direct Moves of Players to the EPL from Non-EPL Clubs 315 8.4 Moves from EPL Clubs 318 8.4.1 The 1992--1996 Time Slice Flows with at Least Three Moves 318 8.4.2 The 1997--2001 Time Slice Flows with at Least Three Moves 322 8.4.3 The 2002--2006 Time Slice Flows with at Least Three Moves 323 8.5 Moves Solely Within the EPL 324 8.5.1 Loans 324 8.5.2 Transfers 326 8.6 All Trails of Footballers to the EPL 330 8.6.1 Counted Features of Trails to the EPL 331 8.6.2 Clustering Player Trails 335 8.6.3 Interpreting the Clusters of Player Careers 350 8.7 Summary and Conclusions 350 9 Mapping Spatial Diversity in the United States of America 353 9.1 Mapping Nations as Spatial Units of the United States 354 9.1.1 The Counties of the United States 357 9.2 Representing Networks in Space 359 9.3 Clustering with a Relational Constraint 360 9.3.1 Conditions for Hierarchical Clustering Methods 361 9.3.2 Clustering with a Relational Constraint 363 9.3.3 An AgglomerativeMethod for Relational Constraints 365 9.3.4 Hierarchies 367 9.3.5 Fast Agglomerative Clustering Algorithms 368 9.4 Data for Constrained Spatial Clustering 369 9.4.1 Discriminant Analysis for Garreau’s Nations 369 9.5 Clustering the US Counties with a Spatial Relational Constraint 374 9.5.1 The Eight Garreau Nations in the USA 375 9.5.2 The Ten Woodard Nations in the USA 379 9.6 Summary 381 10 On Studying Large Networks 382 10.1 Substance 382 10.2 Methods, Techniques, and Algorithms 384 10.3 Network Data 385 10.4 Surprises and Issues Triggered by Them 388 10.5 FutureWork 390 10.6 Two Final Comments 393 Appendix: Data Documentation 395 A.1 Bibliographic Networks 395 A.1.1 Centrality Literature Networks 397 A.1.2 SNA Literature 399 A.2 Patent Data 400 A.3 Supreme Court Data 401 A.4 Football Data 403 A.4.1 Core Data 403 A.4.2 Ancillary Data 413 A.5 The USA Spatial County Network 415 References 419 Person Index 428 Subject Index 432

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Book SynopsisGenetic and Evolutionary Computation: Medical Applications provides an overview of the range of GEC techniques being applied to medicine and healthcare in a context that is relevant not only for existing GEC practitioners but also those from other disciplines, particularly health professionals. There is rapidly increasing interest in applying evolutionary computation to problems in medicine, but to date no text that introduces evolutionary computation in a medical context. By explaining the basic introductory theory, typical application areas and detailed implementation in one coherent volume, this book will appeal to a wide audience from software developers to medical scientists. Centred around a set of nine case studies on the application of GEC to different areas of medicine, the book offers an overview of applications of GEC to medicine, describes applications in which GEC is used to analyse medical images and data sets, derive advanced models, and suggest diagnoses and Table of ContentsAbout the Editors. List of Contributors. 1 Introduction. 2 Evolutionary Computation: A Brief Overview (Stefano Cagnoni and Leonardo Vanneschi). 2.1 Introduction. 2.2 Evolutionary Computation Paradigms. 2.2.1 Genetic Algorithms. 2.2.2 Evolution Strategies. 2.2.3 Evolutionary Programming. 2.2.4 Genetic Programming. 2.2.5 Other Evolutionary Techniques. 2.2.6 Theory of Evolutionary Algorithms. 2.3 Conclusions. 3 A Review of Medical Applications of Genetic and Evolutionary Computation (Stephen L. Smith). 3.1 Medical Imaging and Signal Processing. 3.1.1 Overview. 3.1.2 Image Segmentation. 3.1.3 Image Registration, Reconstruction and Correction. 3.1.4 Other Applications. 3.2 Data Mining Medical Data and Patient Records. 3.3 Clinical Expert Systems and Knowledge-based Systems. 3.4 Modelling and Simulation of Medical Processes. 3.5 Clinical Diagnosis and Therapy. 4 Applications of GEC in Medical Imaging. 4.1 Evolutionary Deformable Models for Medical Image Segmentation: A Genetic Algorithm Approach to Optimizing Learned, Intuitive, and Localized Medial-based Shape Deformation (Chris McIntosh and Ghassan Hamarneh). 4.1.1 Introduction. 4.1.1.1 Statistically Constrained Localized and Intuitive Deformations. 4.1.1.2 Genetic Algorithms. 4.1.2 Methods. 4.1.2.1 Population Representation. 4.1.2.2 Encoding the Weights for GAs. 4.1.2.3 Mutations and Crossovers. 4.1.2.4 Calculating the Fitness of Members of the GA Population. 4.1.3 Results. 4.1.4 Conclusions. 4.2 Feature Selection for the Classification of Microcalcifications in Digital Mammograms using Genetic Algorithms, Sequential Search and Class Separability (Santiago E. Conant-Pablos, Rolando R. Hernández-Cisneros, and Hugo Terashima-Marín). 4.2.1 Introduction. 4.2.2 Methodology. 4.2.2.1 Pre-processing. 4.2.2.2 Detection of Potential Microcalcifications (Signals). 4.2.2.3 Classification of Signals into Microcalcifications. 4.2.2.4 Detection of Microcalcification Clusters. 4.2.2.5 Classification of Microcalcification Clusters into Benign and Malignant. 4.2.3 Experiments and Results. 4.2.3.1 From Pre-processing to Signal Extraction. 4.2.3.2 Classification of Signals into Microcalcifications. 4.2.3.3 Microcalcification Clusters Detection and Classification. 4.2.4 Conclusions and Future Work. 4.3 Hybrid Detection of Features within the Retinal Fundus using a Genetic Algorithm (Vitoantonio Bevilacqua, Lucia Cariello, Simona Cambo, Domenico Daleno, and Giuseppe Mastronardi). 4.3.1 Introduction. 4.3.2 Acquisition and Processing of Retinal Fundus Images. 4.3.2.1 Retinal Image Acquisition. 4.3.2.2 Image Processing. 4.3.3 Previous Work. 4.3.4 Implementation. 4.3.4.1 Vasculature Extraction. 4.3.4.2 A Genetic Algorithm for Edge Extraction. 4.3.4.3 Skeletonization Process. 4.3.4.4 Experimental Results. 5 New Analysis of Medical Data Sets using GEC. 5.1 Analysis and Classification ofMammography Reports using Maximum Variation Sampling (Robert M. Patton, Barbara G. Beckerman, and Thomas E. Potok). 5.1.1 Introduction. 5.1.2 Background. 5.1.3 Related Works. 5.1.4 Maximum Variation Sampling. 5.1.5 Data. 5.1.6 Tests. 5.1.7 Results & Discussion. 5.1.8 Summary. 5.2 An Interactive Search for Rules in Medical Data using Multiobjective Evolutionary Algorithms (Daniela Zaharie, D. Lungeanu, and Flavia Zamfirache). 5.2.1 Medical Data Mining. 5.2.2 Measures for Evaluating the Rules Quality. 5.2.2.1 Accuracy Measures. 5.2.2.2 Comprehensibility Measures. 5.2.2.3 Interestingness Measures. 5.2.3 Evolutionary Approaches in Rules Mining. 5.2.4 An Interactive Multiobjective Evolutionary Algorithm for Rules Mining. 5.2.4.1 Rules Encoding. 5.2.4.2 Reproduction Operators. 5.2.4.3 Selection and Archiving. 5.2.4.4 User Guided Evolutionary Search. 5.2.5 Experiments in Medical Rules Mining. 5.2.5.1 Impact of User Interaction. 5.2.6 Conclusions. 5.3 Genetic Programming for Exploring Medical Data using Visual Spaces (Julio J. Valdés, Alan J. Barton, and Robert Orchard). 5.3.1 Introduction. 5.3.2 Visual Spaces. 5.3.2.1 Visual Space Realization. 5.3.2.2 Visual Space Taxonomy. 5.3.2.3 Visual Space Geometries. 5.3.2.4 Visual Space Interpretation Taxonomy. 5.3.2.5 Visual Space Characteristics Examination. 5.3.2.6 Visual Space Mapping Taxonomy. 5.3.2.7 Visual Space Mapping Computation. 5.3.3 Experimental Settings. 5.3.3.1 Implicit Classical Algorithm Settings. 5.3.3.2 Explicit GEP Algorithm Settings. 5.3.4 Medical Examples. 5.3.4.1 Data Space Examples. 5.3.4.2 Semantic Space Examples. 5.3.5 Future Directions. 6 Advanced Modelling, Diagnosis and Treatment using GEC. 6.1 Objective Assessment of Visuo-spatial Ability using Implicit Context Representation Cartesian Genetic Programming (Michael A. Lones and Stephen L. Smith). 6.1.1 Introduction. 6.1.2 Evaluation of Visuo-spatial Ability. 6.1.3 Implicit Context Representation CGP. 6.1.4 Methodology. 6.1.4.1 Data Collection. 6.1.4.2 Evaluation. 6.1.4.3 Parameter Settings. 6.1.5 Results. 6.1.6 Conclusions. 6.2 Towards an Alternative to Magnetic Resonance Imaging for Vocal Tract Shape Measurement using the Principles of Evolution (David M. Howard, Andy M. Tyrrell, and Crispin Cooper). 6.2.1 Introduction. 6.2.2 Oral Tract Shape Evolution. 6.2.3 Recording the Target Vowels. 6.2.4 Evolving Oral Tract Shapes. 6.2.5 Results. 6.2.5.1 Oral Tract Areas. 6.2.5.2 Spectral Comparisons. 6.2.6 Conclusions. 6.3 How Genetic Algorithms can Improve Pacemaker Efficiency (Laurent Dumas and Linda El Alaoui). 6.3.1 Introduction. 6.3.2 Modeling of the Electrical Activity of the Heart. 6.3.3 The Optimization Principles. 6.3.3.1 The Cost Function. 6.3.3.2 The Optimization Algorithm. 6.3.3.3 A New Genetic Algorithm with a Surrogate Model. 6.3.3.4 Results of AGA on Test Functions. 6.3.4 A Simplified Test Case for a Pacemaker Optimization. 6.3.4.1 Description of the Test Case. 6.3.4.2 Numerical Results. 6.3.5 Conclusion. 7 The Future for Genetic and Evolutionary Computation in Medicine: Opportunities, Challenges and Rewards. 7.1 Opportunities. 7.2 Challenges. 7.3 Rewards. 7.4 The Future for Genetic and Evolutionary Computation in Medicine. Appendix: Introductory Books and Useful Links. Index.

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Book SynopsisAn expert guide for applying data mining with uncertain reasoning to a wide range of uses This volume presents a holistic view of data mining by integrating this diverse and exciting field with uncertain reasoning. It treats a wide range of issues and examines the state of the art in both fields while summarizing vital concepts that can normally only be found in various separate resources. The author concentrates on practical aspects of data mining-such as infrastructure and overall processes-but also discusses some selected algorithms and performance-related issues. Several important topics are addressed specifically, such as bridging the fields of machine learning and data mining and the discovery of influential association rules. In addition, the author discusses data warehousing as an enabling technique for data mining. Case studies are included throughout to illustrate important concepts. Data Mining and Uncertain Reasoning is a practical reference for pTable of ContentsWhat This Book Is About. Basics of Data Mining. Enabling Techniques and Advanced Features of Data Mining. Dealing with Uncertainty in Manipulation of Data. Data Mining Tasks for Knowledge Discovery. Bayesian Networks and Artificial Neural Networks. Uncertain Reasoning Techniques for Data Mining. Data Mining Lifecycle with Uncertainty Handling: Case Studies and Software Tools. Intelligent Conceptual Query Answering with Uncertainty: Basic Aspects and Case Studies. References. Index.

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Book SynopsisWith a strong emphasis on applications of intelligent control, this extremely accessible book covers the fundamentals, methodologies, architectures and algorithms of automatic control systems.Table of ContentsFundamental Techniques for Intelligent Control. Learning Strategies and Algorithms. System Modeling and Estimation. Dynamic Controls. Optimization Control Techniques. Multivariate Statistics and Quality Control. Fault Detection and Diagnosis. Appendix. Bibliography. Index.

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Book SynopsisExploring the chasm between the tyranny of surveillance and the ideal of privacy, this book traces the origins of personal data collection in digital technologies including artificial intelligence (AI) embedded in social network sites, search engines, mobile apps, the web, and email.

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Book SynopsisThis well-balanced, comprehensive and engaging review surveys the development of Artificial Intelligence over the last sixty years and highlights the likely transformative effects of AI on society over the next few decades.Trade Review'Incredibly interesting and comprehensive … five stars' - How It WorksTable of ContentsIntroduction • 1. The Development of AI • 2. The Capabilities of AI Today • 3. Limitations and Problems of AI Today • 4. The Future of AI • Conclusion

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Book SynopsisArtificial Life is the brainchild of scientists who view self-replicating computer programs - such as computer viruses - as new forms of life. This book looks at the social and simulated worlds of Artificial Life - primarily at the Santa Fe Institute, a well-known center for studies in the sciences of complexity.Table of ContentsPreface to the Paperback Edition Acknowledgments Introduction I Simulation in Santa Fe 2 The Word for World Is Computer 3 Inside and Outside the Looking-Glass Worlds of Artificial Life 4 Concerning the Spiritual in Artificial Life 5 Artificial Life in a Worldwide Web Coda Notes References IndexX

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Book SynopsisMultiagent systems combine multiple autonomous entities, each having diverging interests or different information. This overview of the field offers a computer science perspective, but also draws on ideas from game theory, economics, operations research, logic, philosophy and linguistics. It will serve as a reference for researchers in each of these fields, and be used as a text for advanced undergraduate or graduate courses. The authors emphasize foundations to create a broad and rigorous treatment of their subject, with thorough presentations of distributed problem solving, game theory, multiagent communication and learning, social choice, mechanism design, auctions, cooperative game theory, and modal logics of knowledge and belief. For each topic, basic concepts are introduced, examples are given, proofs of key results are offered, and algorithmic considerations are examined. An appendix covers background material in probability theory, classical logic, Markov decision processes andTrade Review'… an excellent volume … It is the first book I have read that brings together the relevant mathematical results from such a wide variety of underlying disciplines. The writing is very clear, and the production standard is excellent … an invaluable reference manual for graduate students and researchers working on these topics … The price is appropriate for a volume of this type, especially as the book serves both to educate the reader and to serve as a reference manual.' Journal of the Operational Research SocietyTable of Contents1. Distributed constraint satisfaction; 2. Distributed optimization; 3. Introduction to non-cooperative game theory; 4. Computing solution concepts of normal-form games; 5. Games with sequential actions; 6. Richer representations; 7. Learning and teaching; 8. Communication; 9. Aggregating preferences; 10. Protocols for strategic agents; 11. Protocols for multiagent resource allocation; 12. Teams of selfish agents; 13. Logics of knowledge and belief; 14. Beyond belief.

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Book SynopsisThe sheer complexity of computer systems has meant that automated reasoning, i.e. the ability of computers to perform logical inference, has become a vital component of program construction and of programming language design. This book meets the demand for a self-contained and broad-based account of the concepts, the machinery and the use of automated reasoning. The mathematical logic foundations are described in conjunction with practical application, all with the minimum of prerequisites. The approach is constructive, concrete and algorithmic: a key feature is that methods are described with reference to actual implementations (for which code is supplied) that readers can use, modify and experiment with. This book is ideally suited for those seeking a one-stop source for the general area of automated reasoning. It can be used as a reference, or as a place to learn the fundamentals, either in conjunction with advanced courses or for self study.Trade Review'Contemporary research in computer science has produced an abundance of formal methods designed to enable hardware and software systems to reason correctly, and to enable us to reason better about these systems. Indeed, the explosion of research and specialised techniques can make it hard for students and newcomers to enter the field. John Harrison's Handbook of Practical Logic and Automated Reasoning is a significant addition to the expository literature on the subject, and will serve as a valuable resource for beginners and experts alike.' Theory and Practice of Logic Programming'John Harrison … has written what clearly will be the book about automation in theorem proving. People often ask me whether they should buy this book. My answer … always is: yes, of course you should buy this book. It is a masterpiece.' Journal of Automated ReasoningTable of ContentsPreface; Ideological orientation; Acknowledgements; How to read this book; 1. Introduction; 2. Propositional logic; 3. First-order logic; 4. Equality; 5. Decidable problems; 6. Interactive theorem proving; 7. Limitations; Appendix 1. Mathematical background; Appendix 2. OCaml made light of; Appendix 3. Parsing and printing of formulas; References; Index.

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Book SynopsisTwenty-five of the most important scientific minds gather for an unparalleled round-table examination about AI and what it means for our future.

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Book SynopsisAn Economist BEST BOOK OF THE YEARAs the data economy grows in power, Carissa Véliz exposes how our privacy is eroded by big tech and governments, why that matters and what we can do about it.The moment you check your phone in the morning you are giving away your data. Before you''ve even switched off your alarm, a whole host of organisations have been alerted to when you woke up, where you slept, and with whom. As you check the weather, scroll through your ''suggested friends'' on Facebook, you continually compromise your privacy.Without your permission, or even your awareness, tech companies are harvesting your information, your location, your likes, your habits, and sharing it amongst themselves. They''re not just selling your data. They''re selling the power to influence you. Even when you''ve explicitly asked them not to. And it''s not just you. It''s all your contacts too.Digital technology is stealing oTrade ReviewA bracing call to arms to fight back against digital surveillance before it is too late. If you're one of those readers who gave up before getting to the end of The Age of Surveillance Capitalism, Shoshama Zoboff's academic doorstopper, this is a good place to start. -- Richard Waters * Financial Times *I have read a *lot* of books on privacy and the surveillance economy. This is the best I've ever found. Lovely clear writing backed by an Oxford don's intellectual firepower. Could not recommend it more. * Barton Gellman, Author of Dark Mirror *An essential guide to one of the most pressing modern issues. * Hannah Fry, author of Hello World *An Economist BEST BOOK OF THE YEAR: galvanises an important conversation. * Economist *We didn't see digital surveillance coming, but today it's threatening democracy and basic freedoms. If you want to understand why privacy matters more than ever before, and how we can preserve it in an age of data grabbing, read this book. * Nigel Warburton, author of A Little History of Philosophy *Powerful -- Will Hutton * Observer *A forceful call for us to tame the data economy by reclaiming our privacy ... and our power. * Jonathan Zittrain, author of The Future of the Internet *Privacy is Power is an intelligent, persuasive and disquieting manifesto for taking back control of our data. -- Cathleen Mair * The Idler *In this bold, original, and engaging book, Carissa Véliz makes a compelling case for the revolutionary goal of reclaiming privacy from the grip of a destructive data economy. While many have lamented the ills of surveillance capitalism, Véliz's courageous manifesto paves a clear path for regaining power--taking back our ill-gotten information from tech companies and data brokers and reinvigorating democracy in the process. * Evan Selinger, Professor of Philosophy, Rochester Institute of Technology and co-author of The Cambridge Handbook of Consumer Privacy *How much does it matter that every day we unwittingly hand over more and more of our personal data to internet giants? In this smart, stylishly written, and alarming volume Carissa Veliz argues that it matters a great deal and that we don't have to put up with it. Essential reading for those of us who click 'agree' ten times a day. * Jonathan Wolff, author of An Introduction to Moral Philosophy *

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Book SynopsisLiving with Robots recounts a foundational shift in robotics, from artificial intelligence to artificial empathy, and foreshadows an inflection point in human evolution. As robots engage with people in socially meaningful ways, social robotics probes the nature of the human emotions that social robots are designed to emulate.Trade ReviewOffers insight into problems raised by advances in robotics and artificial intelligence that will be faced by future societies. Throughout the book, the authors provide a conceptual framework for thinking about possible scenarios of human–robot interactions, most extensively with regard to our relationships with social robots… Living with Robots will meet various expectations, uniting the intellectual depth of a carefully documented academic treatise with the pleasure of a casual page-turner. Those in search of cultural erudition are provided with myriad references to books and movies, and those with a taste for technical novelty are treated to fascinating descriptions of the most hi-tech social robots. -- Paula Quinon * Science *A thoughtful and engaging discussion about an emerging area in applied ethics—social robotics… A timely and well-written volume that addresses many contemporary and future moral questions regarding how we treat artificial intelligence. -- William Simkulet * Library Journal *A very substantial philosophical study. * Philosophie Magazine *One should not lose sight of the prospective and speculative aspect of the research and ideas of Dumouchel and Damiano. But their work is nevertheless remarkably profound and intelligent, and it provides us, as do all serious inquiries into robotics, with a better understanding of ourselves, especially the social aspect of our minds. Even if one might doubt that social robots could ever decipher the incredible complexity of our feelings and adapt to them, this project nevertheless represents a fascinating step, less in robotics itself than in the quest for the human mind to understand itself. * Le Temps *Living with Robots is a convincing reflection on the increasing presence of robots in society. Designed to operate in an environment shaped and occupied by humans, robots are the new actors in a technical, social, and cultural transformation. The book offers a distinctive and fruitful approach to social robotics through different theoretical frameworks, analyzing the implications of interactions between humans and robots, between humans via robots, and between robots themselves. -- Zaven Paré, Rio de Janeiro State UniversityLiving with Robots is a timely and fascinating examination of social robots that exist in the real world, have bodies, and interact with human beings. While addressing the practical functions of social robots, at its heart the book is deeply philosophical. The authors invite us to reflect on the nature of human beings, mind, and sociability, as well as the human–robot dynamics of emotional relationships. This gives rise to novel and important engagement with moral and political questions, from quality of life to military applications. -- Takanori Shibata, National Institute of Advanced Industrial Science and Technology[Dumouchel and Damiano’s] book takes us on a detailed tour of the philosophy of artificial intelligence (AI)—especially as it applies to robots intended to build social relationships with humanity. This is a work of serious scholarship, with arguments about identity, authority, autonomy and what is termed ‘artificial empathy’ presented with reference to a range of example systems. Kant, Descartes, Hobbes and other philosophical heavyweights get the exposure you might expect, but when set alongside the views of such disparate players as psychologist Jean Piaget and science-fiction writer Algis Budrys the analysis offers considerable breadth…If we are to build a robust, appropriate ethical structure around the next generation of technical development—some combination of deep learning, artificial intelligence, robotics and artificial empathy—we need to understand that managing the impact of these technologies is far too important to be left to those who are enthusiastically engaged in producing them. This book is both a comprehensive, engaging review of philosophical thought and a warning to anyone who thinks that the integration of robotics into our society is about technology alone. -- John Gilbey * Times Higher Education *

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Book SynopsisTrade Review"One of The Guardian’s Favourite Reads of 2017 as chosen by scientists"

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Book SynopsisAn exploration of the relationship between Marxist theory and Artificial Intelligence.Trade Review'Indispensable reading for all those who want to understand the relationship between artificial intelligence and capitalism' -- Christian Fuchs, author of 'Digital Demagogue: Authoritarian Capitalism in the Age of Trump and Twitter''A radical and provocative reading. The authors' criticisms of left accelerationism are timely and persuasive, their conclusion is bracing but necessary.' -- Sarah Kember, author of 'iMedia: The Gendering of Objects, Environments and Smart Materials''A fascinating and pioneering work that deploys Marx to understand contemporary AI capitalism. An exemplary contribution to understanding how machine learning is changing our world and transforming communist strategy' -- Nick Srnicek, author of 'Platform Capitalism''A disturbing but essential addition to the rapidly growing literature on the risks posed by capitalist-conceived AI' -- Morning StarTable of ContentsSeries Preface Acknowledgements Introduction: AI-Capital 1. Means of Cognition 2. Automating the Social Factory 3. Perfect Machines, Inhuman Labour Conclusion: Communist AI Notes Bibliography Index

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Book SynopsisAn exploration of the relationship between Marxist theory and Artificial Intelligence.Trade Review'Indispensable reading for all those who want to understand the relationship between artificial intelligence and capitalism' -- Christian Fuchs, author of 'Digital Demagogue: Authoritarian Capitalism in the Age of Trump and Twitter''A radical and provocative reading. The authors' criticisms of left accelerationism are timely and persuasive, their conclusion is bracing but necessary.' -- Sarah Kember, author of 'iMedia: The Gendering of Objects, Environments and Smart Materials''A fascinating and pioneering work that deploys Marx to understand contemporary AI capitalism. An exemplary contribution to understanding how machine learning is changing our world and transforming communist strategy' -- Nick Srnicek, author of 'Platform Capitalism''A disturbing but essential addition to the rapidly growing literature on the risks posed by capitalist-conceived AI' -- Morning StarTable of ContentsSeries Preface Acknowledgements Introduction: AI-Capital 1. Means of Cognition 2. Automating the Social Factory 3. Perfect Machines, Inhuman Labour Conclusion: Communist AI Notes Bibliography Index

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Book SynopsisCould a computer have a mind? What kind of machine would this be? Exactly what do we mean by ''mind'' anyway?The notion of the ''intelligent'' machine, whilst continuing to feature in numerous entertaining and frightening fictions, has also been the focus of a serious and dedicated research tradition. Reflecting on these fictions, and on the research tradition that pursues ''Artificial Intelligence'', raises a number of vexing philosophical issues. Minds and Computers introduces readers to these issues by offering an engaging, coherent, and highly approachable interdisciplinary introduction to the Philosophy of Artificial Intelligence.Readers are presented with introductory material from each of the disciplines which constitute Cognitive Science: Philosophy, Neuroscience, Psychology, Computer Science, and Linguistics. Throughout, readers are encouraged to consider the implications of this disparate and wide-ranging material for the possibility of developing machines with minds. And they can expect to de

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Book SynopsisDaniel Newman is Principal Analyst of Futurum Research and the CEO of Broadsuite Media Group. Living his life at the intersection of people and technology, Newman works with large technology brands exploring digital transformation and how it influences our world. A five-times best-selling author, he is a Forbes, Entrepreneur and Huffington Post contributor.Olivier Blanchard draws from his extensive experience managing product innovation, technology adoption, digital integration and change management for industry leaders in a variety of sectors to help decision makers, and their organizations, understand the many risks and opportunities of technology-driven disruption and leverage innovation.Table of Contents Chapter - 01: A short history of human–machine partnerships; Chapter - 02: The state of human–machine partnerships; Chapter - 03: Framing expectations for the next age of human–machine partnerships; Chapter - 04: How businesses should prepare for the next age of human–machine partnerships; Chapter - 05: How workers should prepare for the next age of human–machine partnerships; Chapter - 06: How educational institutions should prepare for the next age of human–machine partnerships; Chapter - 07: How consumers should prepare for the next age of human–machine partnerships; Chapter - 08: How technology companies should prepare for the next age of human–machine partnerships; Chapter - 09: The future of human–machine partnerships - putting it all together;

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Book SynopsisA Financial Times and Economist Book of the Year''Wonderfully stimulating... will teach you to see around corners'' - TIM HARFORD''A paean to cognitive agility and the elasticity of the imagination'' - ECONOMIST''Captivating... will transform the way you think'' MARISSA KING, PROFESSOR AT YALE SCHOOL OF MANAGEMENTThe power of mental models to make better decisionsWe''re often told that humans make bad decisions and that more data is better. But this is backwards: people are good at decisions precisely because we use mental models and can envision new realities outside of data. Great outcomes don''t depend so much on the final moment of choosing but on generating better alternatives to choose between. That''s framing. It''s a cognitive muscle we can strengthen to improve our lives, work and future -- to meet this historical moment. Framers shows how.Trade ReviewA tightly written prescription for smart thinking... A bold call to reinject pluralism and progressive human values into a decision-making process dominated by algorithms or gut instinct * Financial Times -- 2021 Summer Books List *A simply wonderful reimagining of human ingenuity. It will teach you to see around corners * Tim Harford, bestselling author of How To Make The World Add Up *Reveals how you can recognize the lenses that you're applying ... an important read -- a steady hand for our turbulent times * Adam Grant, #1 New York Times bestselling author of THINK AGAIN *A fascinating look at what makes humans special and how people can improve the way they think to stay ahead of the machines * Mustafa Suleyman, Co-founder of DeepMind, Vice-President of Google *A paean to cognitive agility and the elasticity of the imagination * The Economist *Packed with big ideas, great stories, values and verve that make it a delight to read. It will certainly change how you think-and might just change the world too * Annie Duke, best-selling author of THINKING IN BETS and former poker champion *A great book filled with fresh perspectives to help us out during the rise of AI so we can usher in the Age of Humanity * will.i.am, musician and entrepreneur *A captivating read... Framers will transform the way you think * Marissa King, Professor at Yale School of Management *Brilliantly shows that mental models are at the heart of creativity, critical thinking and innovation, and how we can get better at it to solve our toughest business and social challenges * Aaron Levie, CEO of Box *Provides insight into how we can all nurture more of a beginner's mind and manifest breakthrough ideas for building a better future * Marc Benioff, chairman and CEO, Salesforce *An appealing pop-science guide to creativity [and] an astute analysis of problem-solving * Kirkus Reviews *Humans today live a much easier, more comfortable, and more pleasant existence than ever before, but we also face our greatest existential challenges as a species, and in order to survive we will need not only cooperation, but a new and diverse set of cognitive frames ... [this book] teaches us how to better frame things for ourselves as individuals * Porchlight BOOK TO WATCH *

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Book SynopsisThe result of the selection of papers presented at a special session entitled 'Applications of Artificial Intelligence in Economics and Finance' at the '2003 International Conference on Artificial Intelligence'. This volume will appeal to economists interested in adopting an interdisciplinary approach to the study of economic problems.Table of Contents1. Statistical analysis of genetic algorithms in discovering technical trading strategies (S.H. Chen, C.Y. Tsao). 2. A genetic programming approach to model international short-term capital flow (T. Yu, S.H. Chen, T.W. Kuo). 3. Tools for non-linear time series forecasting in economics: An empirical comparison of regime switching vector autoregressive models and recurrent neural networks (J.M. Binner, T. Elger, B. Nilsson, J.A. Tepper). 4. Using non-parametric search algorithms to forecast daily excess stock returns (N.L. Joseph, D.S. Bree, E. Kalyvas). 5. Co-evolving neural networks with evolutionary strategies: A new application to Divisia Money (J. Binner, G. Kendall, A. Gazely). 6. Forecasting the EMU inflation rate: Linear econometric versus non-linear computational models using genetic neural fuzzy systems (S. Kooths, T. Mitze, E. Ringhut). 7. Finding or not finding rules in time series (J. Lin, E. Keogh). 8. A comparison of VAR and neural networks with genetic algorithm in forecasting price of oil (S. Mirmirani, H.C. Li). 9. Searching for Divisia/Inflation Relationships with the aggregate feed forward neural network (V.A. Schmidt, J.M. Binner). 10. Predicting housing value: Genetic algorithm attribute selection and dependence modelling utilising the gamma test (I.D. Wilson, A. J. Jones, D.H. Jenkins, J.A. Ware).

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Book SynopsisUsing examples drawn from biomedicine and biomedical engineering, this essential reference book brings you comprehensive coverage of all the major techniques currently available to build computer-assisted decision support systems. You will find practical solutions for biomedicine based on current theory and applications of neural networks, artificial intelligence, and other methods for the development of decision aids, including hybrid systems. Neural Networks and Artificial Intelligence for Biomedical Engineering offers students and scientists of biomedical engineering, biomedical informatics, and medical artificial intelligence a deeper understanding of the powerful techniques now in use with a wide range of biomedical applications. Highlighted topics include: Types of neural networks and neural network algorithms Knowledge representation, knowledge acquisition, and reasoning methodologies Chaotic analysis of biomedical time series Table of ContentsPreface. Acknowledgments. Overview. NEURAL NETWORKS. Foundations of Neural Networks. Classes of Neural Networks. Classification Networks and Learning. Supervised Learning. Unsupervised Learning. Design Issues. Comparative Analysis. Validation and Evaluation. ARTIFICIAL INTELLIGENCE. Foundation of Computer-Assisted Decision Making. Knowledge Representation. Knowledge Acquisition. Reasoning Methodologies. Validation and Evaluation. ALTERNATIVE APPROACHES. Genetic Algorithms. Probabilistic Systems. Fuzzy Systems. Hybrid Systems. HyperMerge, a Hybird Expert System. Future Perspectives. Index. About the Authors.

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Book SynopsisDetailed treatments of particular NFRs - accuracy, security and performance requirements - along with treatments of NFRs for information systems are presented as specializations of the NFR Framework.Table of ContentsList of Figures. List of Tables. Legend for Figures. Preface. 1. Introduction. Part I: The NFR Framework. 2. The NFR Framework in Action. 3. Softgoal Interdependency Graphs. 4. Cataloguing Refinement Methods and Correlations. Part II: Types of Non-Functional Requirements. 5. Types of NFRs. 6. Accuracy Requirements. 7. Security Requirements. 8. Performance Requirements. 9. Performance Requirements for Information Systems. Part III. Case Studies and Applications. 10. Introduction to the Studies and Applications. 11. A Credit Card System. 12. An Administrative System. 13. Application to Software Architecture. 14. Enterprise Modelling and Business Process Redesign. 15. Assessment of Studies. Postscript. Bibliography.

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Book SynopsisBringing together a comprehensive and diverse collection of research, theory, and thought, this volume builds a foundation for the new field of Augmented Cognition research and development. The first section introduces general Augmented Cognition methods and techniques, including physiological and neurophysiological measures such as EEG and fNIR; adaptive techniques; and sensors and algorithms for cognitive state estimation. The second section discusses Augmented Cognition applications such as simulation and training, intent-driven user interfaces, closed-loop command and control systems, then goes on to explore lessons learned to date, and future directions in Augmented Cognition-enabled HCI.Table of ContentsContents: Part I: Human Information Processing.Part II: Cognitive State Sensors.Part III: Augmented Cognition Technology.Part IV: Augmented Cognition and Advanced Computing.Part V: AugCog New Directions.

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