Artificial intelligence (AI) Books

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.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

Book SynopsisThis book is designed to provide in-depth knowledge on how search plays a fundamental role in problem solving. Meant for undergraduate and graduate students pursuing courses in computer science and artificial intelligence, it covers a wide spectrum of search methods. Readers will be able to begin with simple approaches and gradually progress to more complex algorithms applied to a variety of problems. It demonstrates that search is all pervasive in artificial intelligence and equips the reader with the relevant skills. The text starts with an introduction to intelligent agents and search spaces. Basic search algorithms like depth first search and breadth first search are the starting points. Then, it proceeds to discuss heuristic search algorithms, stochastic local search, algorithm A*, and problem decomposition. It also examines how search is used in playing board games, deduction in logic and automated planning. The book concludes with a coverage on constraint satisfaction.

Read more less

Book SynopsisThis book gives legal practitioners, academics, and law students a comprehensive look at the main impacts of artificial intelligence use in legal practice. Contributors identify the main challenges surrounding a legally compliant and ethical development of AI and craft a framework for analyzing the costs and benefits of new technology.Table of Contents1. Lawyering in the Digital Age Pietro Ortolani and Larry A. DiMatteo; Part I. Effects of Technology on Legal Practice: 2. Disruptive effects of legal tech Larry A. DiMatteo, Jiang Christine Jiaying and Robert Thomas; 3. The effects of technology on legal practice: from punch card to artificial intelligence? Andrė Janssen and Tom J. Vennmanns; 4. Legal drafting and automation Benjamin Werthmann; 5. Emerging rules on artificial intelligence: Trojan horses of ethics in the realm of law? Florian Möslein and Maximilian Horn; Part II. Legal Tech and ADR: 6. Legal tech in ADR Mateja Durovic and Franciszek Lech; 7. A blockchain-based smart dispute resolution method Alessandro Palombo, Raffaele Battaglini and Luigi Cantisani; 8. Digital dispute resolution: blurring the boundaries of ADR Pietro Ortolani; Part III. Legal Tech in Consumer Relations and Small Claims 9. Legal tech in consumer relations and small-value claims: a survey Francisco de Elizalde; 10. Regulation of legal services and access to justice in the digital age: a war report Jin-Ho Verdonschot and Max Houben; 11. Legal tech and EU consumer law Martin Ebers; 12. The two faces of legal tech in B2C relations Eric Tjong Tjin Tai; Part IV. Legal Tech and Public Law: 13. Blockchain's heterotopia: technological infrastructures and lawyering in the public sector Georgios Dimitropoulos; 14. Fundamental rights and the use of artificial intelligence in court Jean-Marc van Gyseghem; 15. Legal tech in public administration: prospects and challenges Antonios Kouroutakis; Part V. Legal Ethics and Societal Values Confront Technology: 16. Ethics guidelines for trustworthy AI Michel Cannarsa; 17. Ethical digital lawyering: technical and philosophical insights Mathieu Guillermin, Arnaud Billion, Carine Copain-Héritier and Emmanuel de Vaujany; 18. Law, disintermediation, and the future of trust Christoph Kletzer; Part VI. Fate of the Legal Professions: 19. Lawyering somewhere between computation and the will to act: a digital age reflection Jeffrey M. Lipshaw; 20. Surviving the digital transformation – a method for lawyers to approach legal tech Paw Fruerlund and Sebastian Peters; 21. Road forward: promise and danger Larry A. DiMatteo and Pietro Ortolani.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

Book SynopsisWith an increasing number of applications in the context of multi-agent systems, automated negotiation is a rapidly growing area. Written by top researchers in the field, this state-of-the-art treatment of the subject explores key issues involved in the design of negotiating agents, covering strategic, heuristic, and axiomatic approaches. The authors discuss the potential benefits of automated negotiation as well as the unique challenges it poses for computer scientists and for researchers in artificial intelligence. They also consider possible applications and give readers a feel for the types of domains where automated negotiation is already being deployed. This book is ideal for graduate students and researchers in computer science who are interested in multi-agent systems. It will also appeal to negotiation researchers from disciplines such as management and business studies, psychology and economics.Table of ContentsList of illustrations; Preface; Summary of key notation; 1. Introduction; 2. Games in normal form; 3. Games in extensive form; 4. Negotiation domains; 5. Strategic analysis of single-issue negotiation; 6. Strategic analysis of multi-issue negotiation; 7. The negotiation agenda; 8. Multilateral negotiations; 9. Heuristic approaches; 10. Man-machine negotiations; 11. Axiomatic analysis of negotiation; 12. Applications; 13. Related topics; 14. Concluding remarks; Appendix A. Proofs; References; Index.

Read more less

Book SynopsisThis book develops a view of logic as a theory of information-driven agency and intelligent interaction between many agents - with conversation, argumentation and games as guiding examples. It will interest students and scholars in a wide variety of subject areas.Trade Review'Logical Dynamics [of Information and Interaction] is at the frontiers of applied logic. This is an essential book for any student of the subject, written by a master of the field.' Dov Gabbay, King's College London'… this book is the best we can have for now as a great source for the research in the field of logical dynamics of information and interaction. It can be used as a handbook of DEL as well. I think the author has succeeded in demonstrating a new view of logic as a theory of information flow in the interaction of agents.' Yanjing Wang, Studia LogicaTable of ContentsPreface; 1. Logical dynamics, agency, and intelligent interaction; 2. Epistemic logic and semantic information; 3. Dynamic logic of public observation; 4. Multi-agent dynamic-epistemic logic; 5. Dynamics of inference and awareness; 6. Questions and issue management; 7. Soft information, correction, and belief change; 8. An encounter with probability; 9. Preference statics and dynamics; 10. Decisions, actions, and games; 11. Processes over time; 12. Epistemic group structure and collective agency; 13. Logical dynamics in philosophy; 14. Computation as conversation; 15. Rational dynamics in game theory; 16. Meeting cognitive realities; 17. Conclusion; Bibliography.

Read more less

Book SynopsisThis text provides an exceptional introduction to the multidisciplinary field of mobile robotics using hands-on examples in ROS 2 enabling students to explore concepts either in a simulation or using their own robot hardware. The new edition includes coverage of HRI, robot ethics, and AI techniques for end-to-end robot control.Trade Review'This book is an indispensable tool for any - both pre-university and university - course on mobile robotics. In relation to the first edition, this current one has been sufficiently updated. I recommend this book to researchers - particularly those who study localization or mapping - and doctoral students who are interested in investigating the latest approaches and techniques in the mobile robotics field.' Ramon Gonzalez Sanchez, Computing Reviews'… a great resource for an intermediate or advanced course on mobile robotics.' R. S. Stansbury, ChoiceTable of ContentsAcknowledgments; Preface; 1. Overview and motivation; 2. Fundamental problems; Part I. Locomotion and Perception: 3. Mobile robot hardware; 4. Non-visual sensors and algorithms; 5. Visual sensors and algorithms; Part II. Representation and Planning: 6. Deep learning for robots; 7. Planning in, representing and reasoning about space; 8. System control; 9. Pose maintenance and localization; 10. Mapping and related tasks; 11. Robot collectives; 12. Human-robot interaction; 13. Robot ethics; 14. Robots in practice; 15. The future of mobile robotics; Appendix A. Fictional robots; Appendix B. Probability and statistics; Appendix C. Linear systems, matrices and filtering; Appendix D. Markov models; Bibliography; Index.

Read more less

Book SynopsisThe field of artificial intelligence (AI) and the law is on the cusp of a revolution that began with text analytic programs like IBM''s Watson and Debater and the open-source information management architectures on which they are based. Today, new legal applications are beginning to appear and this book - designed to explain computational processes to non-programmers - describes how they will change the practice of law, specifically by connecting computational models of legal reasoning directly with legal text, generating arguments for and against particular outcomes, predicting outcomes and explaining these predictions with reasons that legal professionals will be able to evaluate for themselves. These legal applications will support conceptual legal information retrieval and allow cognitive computing, enabling a collaboration between humans and computers in which each does what it can do best. Anyone interested in how AI is changing the practice of law should read this illuminating wTrade Review'In relation to the composition of this book, it provides a comprehensive and user-friendly description of this interdisciplinary area, focusing on the suitability of developing legal devices based on artificial intelligence. The structure of the work allows users to analyse how representation of legal logic knowledge occurs, and its suitability for computational implementations … On this matter, the author provides relevant and understandable illustrations that facilitate the linkage between theory and the development of the techno legal implementations. … Artificial Intelligence and Legal Analytics: New Tools for Law Practice in the Digital Age is a fundamental work for those of us who are interested in the intersection between intelligent technology and the legal field, and its promising future.' Jesus Manuel Niebla Zatarain, SCRIPTedTable of ContentsPart I. Computational Models of Legal Reasoning: 1. Introducing AI and Law and its role in future legal practice; 2. Modeling statutory reasoning; 3. Modeling case-based legal reasoning; 4. Models for predicting legal outcomes; 5. Computational models of legal argument; Part II. Legal Text Analytics: 6. Representing legal concepts in ontologies and type systems; 7. Making legal informational retrieval smarter; 8. Machine learning with legal texts; 9. Extracting information from statutory and regulatory texts; 10. Extracting argument-related information from legal case texts; Part III. Connecting Computational Reasoning Models and Legal Texts: 11. Conceptual legal information retrieval for cognitive computing; 12. Cognitive computing legal apps.

Read more less

Book SynopsisTrade Review"I hope you, like me, appreciate that Kevin finds some balance between the poles of 'the robots are coming for our jobs!' and 'AI is great, nothing to worry about!' I suspect that, in the coming decades, we'll increasingly recognize the wisdom of that view." — J.D. Vance, New York Times bestselling author of Hillbilly Elegy All good futures for our society and for humanity broadly require harnessing technology. Kevin has written a very important book here. He shows how and why to have optimism about artificial intelligence, harnessing the technology to restore the American Dream. What's the future we should work towards? Kevin's book shines the light forward. Highly recommended. — Reid Hoffman, co-founder of Linkedin and author of Blitzscaling "Kevin's examination of AI and the future of rural communities like the one in which he grew up in rural Virginia provides an unusually personal and empathetic perspective on how we can, together, create opportunity for ourselves, our organizations, and our world." — Satya Nadella, CEO of Microsoft "Scott’s version of our AI future is a refreshingly optimistic one, and it’s coming from someone who knows the technology very, very well." — Fast Company

Read more less

Book Synopsis

Read more less

Book Synopsis

Read more less

Book SynopsisFor fans of computers and comedy alike, an accessible and entertaining look into how we can use artificial intelligence to make smart machines funny.Most robots and smart devices are not known for their joke-telling abilities. And yet, as computer scientist Tony Veale explains in Your Wit Is My Command, machines are not inherently unfunny; they are just programmed that way. By examining the mechanisms of humor and jokes--how jokes actually works--Veale shows that computers can be built with a sense of humor, capable not only of producing a joke but also of appreciating one. Along the way, he explores the humor-generating capacities of fictional robots ranging from B-9 in Lost in Space to TARS in Interstellar, maps out possible scenarios for developing witty robots, and investigates such aspects of humor as puns, sarcasm, and offensiveness. In order for robots to be funny, Veale explains, we need to analyze humor computationally. Using artific

Read more less

Book SynopsisAn in-depth overview of an emerging field that brings together high-performance computing, big data processing, and deep lLearning. Over the last decade, the exponential explosion of data known as big data has changed the way we understand and harness the power of data. The emerging field of high-performance big data computing, which brings together high-performance computing (HPC), big data processing, and deep learning, aims to meet the challenges posed by large-scale data processing. This book offers an in-depth overview of high-performance big data computing and the associated technical issues, approaches, and solutions.  The book covers basic concepts and necessary background knowledge, including data processing frameworks, storage systems, and hardware capabilities; offers a detailed discussion of technical issues in accelerating big data computing in terms of computation, communication, memory and storage, codesign, workload chara

Read more less

Book SynopsisHow to stay in charge in a world populated by algorithms that beat us in chess, find us romantic partners, and tell us to “turn right in 500 yards.”Doomsday prophets of technology predict that robots will take over the world, leaving humans behind in the dust. Tech industry boosters think replacing people with software might make the world a better place—while tech industry critics warn darkly about surveillance capitalism. Despite their differing views of the future, they all seem to agree: machines will soon do everything better than humans. In How to Stay Smart in a Smart World, Gerd Gigerenzer shows why that’s not true, and tells us how we can stay in charge in a world populated by algorithms.Machines powered by artificial intelligence are good at some things (playing chess), but not others (life-and-death decisions, or anything involving uncertainty). Gigerenzer explains why algorithms often fail at finding us romantic part

Read more less

Book Synopsis

Read more less

Book Synopsis

Read more less

Book SynopsisSix classic science fiction stories and commentary that illustrate and explain key algorithms or principles of artificial intelligence.This book presents six classic science fiction stories and commentary that illustrate and explain key algorithms or principles of artificial intelligence. Even though all the stories were originally published before 1973, they help readers grapple with two questions that stir debate even today: how are intelligent robots programmed? and what are the limits of autonomous robots? The stories—by Isaac Asimov, Vernor Vinge, Brian Aldiss, and Philip K. Dick—cover telepresence, behavior-based robotics, deliberation, testing, human-robot interaction, the “uncanny valley,” natural language understanding, machine learning, and ethics. Each story is preceded by an introductory note, “As You Read the Story,” and followed by a discussion of its implications, “After You Have Read the Story.” Together with the

Read more less

Book Synopsis

Read more less

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

Read more less

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

Read more less

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.

Read more less

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.

Read more less

Book SynopsisFrom Oxford''s leading AI researcher comes a fun and accessible tour through the history and future of one of the most cutting edge and misunderstood field in science: Artificial IntelligenceThe somewhat ill-defined long-term aim of AI is to build machines that are conscious, self-aware, and sentient; machines capable of the kind of intelligent autonomous action that currently only people are capable of. As an AI researcher with 25 years of experience, professor Mike Wooldridge has learned to be obsessively cautious about such claims, while still promoting an intense optimism about the future of the field. There have been genuine scientific breakthroughs that have made AI systems possible in the past decade that the founders of the field would have hailed as miraculous. Driverless cars and automated translation tools are just two examples of AI technologies that have become a practical, everyday reality in the past few years, and which will have a huge impact on our w

Read more less

Book Synopsis

Read more less

Book Synopsis

Read more less

Book SynopsisMany organizations recognize the value and benefits Artificial Intelligence (AI) can bring if implemented correctly. This topic is outlined in the authors’ previous book, Artificial Intelligence: Evolution and Revolution. A long-standing challenge that many organizations continue to face is preparing for AI and making sure that their data and assets are accessible, manageable, and governed and are of the right quality so that they can be consumed by new and existing AI applications in order to infuse AI across the enterprise to help drive smarter business outcomes. Over the years, numerous paradigms and efforts have attempted to address the complexities of managing sprawling and disparate data silos, but all seemed to have fallen short of their promises and expectations. Organizations need the flexibility to put their data and assets where it makes most business sense, whether that’ s on premises or in the private or public cloud. This book attempts to explain the concepts and values that a data fabric approach can deliver to both technical and business communities.

Read more less

Book SynopsisBig Data Big Design defines and explores what every designer needs to know about artificial intelligence (AI) and machine learning (ML), all the while inspiring designers to harness this technology and establish leadership via thoughtful, human-centered design. It’s not just about the algorithms, it’s about what we do with the data once received. Ellen lupton says, “ Important and accessible!” Readers will explore the principles and cultural context of Ai and ML, as well as gain an understanding of the design opportunities and pitfalls that arise as designers incorporate predictive algorithms into their practice. Designers will walk away from this portable, friendly book inspired by practical and theoretical knowledge that will allow them to make thoughtful decisions as this technology unfolds.

Read more less

Book SynopsisCountless books, news reports, and opinion pieces have announced the impending arrival of artificial intelligence, with most claiming that it will upend our world, revolutionizing not just work but society overall. Yet according to political philosopher and historian David Runciman, we’ve actually been living with a version of AI for 300 years because states and corporations are robots, too. In The Handover, Runciman explains our current situation through the history of these “artificial agents” we created to rescue us from our all-too-human limitations—and demonstrates what this radical new view of our recent past means for our collective future. From the United States and the United Kingdom to the East India Company, Standard Oil, Facebook, and Alibaba, states and corporations have gradually, and then much more rapidly, taken over the planet. They have helped to conquer poverty and eliminate disease, but also unleashed global wars and environmental degradation. As Runciman demonstrates, states and corporations are the ultimate decision-making machines, defined by their ability to make their own choices and, crucially, to sustain the consequences of what has been chosen. And if the rapid spread of the modern state and corporation has already transformed the conditions of human existence, new AI technology promises the same. But what happens when AI interacts with other kinds of artificial agents, the inhuman kind represented by states and corporations? Runciman argues that the twenty-first century will be defined by increasingly intense battles between state and corporate power for the fruits of the AI revolution. In the end, it is not our own, human relationship with AI that will determine our future. Rather, humanity’s fate will be shaped by the interactions among states, corporations, and thinking machines. With clarity and verve, The Handover presents a brilliantly original history of the last three centuries and a new understanding of the immense challenges we now face.Trade Review"[W]itty and refined . . . Runciman’s basic argument, which unfolds in the elegantly shaggy manner of a Peripatetic seminar, is that the alignment problem is not in fact an anomaly, and that the coming singularity might best be historicized as the Second Singularity. . . . he turns a standard argumentative form on its head. It’s not that we can look to the past to help us solve the alignment problems of the future. It’s that the alignment problems of the future help clarify our existing sense that everything is intractable and wrong. . . . Runciman’s point is that the alliance between even a democratic government and a safe-ish A.I. could derail civilization." -- Gideon Lewis-Kraus - New Yorker"Ingenious . . . a well-informed and provocative read about the essence of political power." -- John Thornhill - Financial Times"[A] searching meditation on creeping dehumanization . . . Runciman’s approach to these issues is less technological than social and psychological . . . The result is a shrewd and stimulating look at society’s drive toward an inhuman perfection." -- Publishers Weekly, starred review"A thoughtful, learned contribution to the fevered conversation now surrounding AI." -- Kirkus Reviews"Amid a headlong international panic about a looming robot insurrection, David Runciman offers a searching history of earlier takeovers by other artificial creatures of our own making—states and corporations—and a stirring call for a new and fortified commitment to all that is human." -- Jill Lepore, author of These Truths: A History of the United States"David Runciman is always fascinating." -- Adam Tooze"One of the great modern writers of democracy." -- Anne Applebaum"Surely one of the most luminously intelligent [writers] on politics to have been published for many years." -- New Statesman (UK)

Read more less

Book Synopsis With the idea of “deep learning” having now become the key to this new generation of solutions, major technological players in the business intelligence sector have taken an interest in the application of Big Data. In this book, the author explores the recent technological advances associated with digitized data flows, which have recently opened up new horizons for AI. The reader will gain insight into some of the areas of application of Big Data in AI, including robotics, home automation, health, security, image recognition and natural language processing. Table of ContentsList of Figures ix Preface xiii Introduction xxi Chapter 1. What is Intelligence? 1 1.1. Intelligence 1 1.2. Business Intelligence 2 1.3. Artificial Intelligence 5 1.4. How BI has developed 6 1.4.1. BI 1.0 7 1.4.2. BI 2.0 8 1.4.3. And beyond 11 Chapter 2. Digital Learning 13 2.1. What is learning? 13 2.2. Digital learning 14 2.3. The Internet has changed the game 16 2.4. Big Data and the Internet of Things will reshuffle the cards 18 2.5. Artificial Intelligence linked to Big Data will undoubtedly be the keystone of digital learning 21 2.6. Supervised learning 22 2.7. Enhanced supervised learning 24 2.8. Unsupervised learning 28 Chapter 3. The Reign of Algorithms 33 3.1. What is an algorithm? 34 3.2. A brief history of AI 34 3.2.1. Between the 1940s and 1950s 35 3.2.2. Beginning of the 1960s 36 3.2.3. The 1970s 37 3.2.4. The 1980s 37 3.2.5. The 1990s 38 3.2.6. The 2000s 38 3.3. Algorithms are based on neural networks, but what does this mean? 39 3.4. Why do Big Data and AI work so well together? 42 Chapter 4. Uses for Artificial Intelligence 47 4.1. Customer experience management 48 4.1.1. What role have smartphones and tablets played in this relationship? 50 4.1.2. CXM is more than just a software package 51 4.1.3. Components of CXM 53 4.2. The transport industry 55 4.3. The medical industry 58 4.4. “Smart” personal assistant (or agent) 60 4.5. Image and sound recognition 62 4.6. Recommendation tools 65 4.6.1. Collaborative filtering (a “collaborative” recommendation mode) 66 Conclusion 71 Appendices 75 Appendix 1. Big Data 77 Appendix 2. Smart Data 83 Appendix 3. Data Lakes 89 Appendix 4. Some Vocabulary Relevant to 93 Appendix 5. Comparison Between Machine Learning and Traditional Business Intelligence 101 Appendix 6. Conceptual Outline of the Steps Required to Implement a Customization Solution based on Machine Learning 103 Bibliography 107 Glossary 111 Index 115

Read more less

Book Synopsis“I love my robot lawn mowers, my laptop, wifi, Google, Facetime, Whatsapp and the possibility of drone postal deliveries and more.. Yet worries nag about being overwhelmed by an artificial intelligence revolution whose ethical and moral parameters are less clear than its rampant profiteering from and monetising of your lives and mine. This hugely informative book shakes us out of our massage armchairs and demands that we engage immediately with these galloping advances so we can shape them to the benefit of the many and not leave them to the enrichment of the few at the awful cost of the impoverishment of swathes of humanity”. Mary McAleese, former President of Ireland. "Robots, Ethics and The Future of Jobs is a wakeup call for political, civic, media and church leaders, urging a response to the deepening and accelerating pace of technological change and its potential consequences. Artificial Intelligence, robotics, drones, the internet of things and 3D printing are the building blocks of the 4th industrial revolution. These technologies offer great potential but also carry real risks and are reaching into every corner of our lives, civilian and military. Who will win and who will lose? Who will set the rules and the ethical boundaries within which they should develop and operate? Will the displaced be included, if so, how; or ignored and, if so, with what political, social and economic consequences? That these questions cannot be avoided and should not be postponed - and that we do not need to wait for change to happen because it is already upon us - are central messages of this thought provoking text." Pat Cox, former President European Parliament.Trade Review‘...required reading for all those who take their faith seriously. This book needs to be widely available, read, and its contents put into practice and earthed.’ Catholic South West Feb21 -- Denis Blackledge SJ * Catholic South West Diocesan magazine *‘...an accessible and thought-provoking look at the potential of new technology to bring about seismic societal change and a call for discourse and decisions to ensure the best outcomes for humanity.’ ICN Feb21 * Independent Catholic News website *‘...this important book...deals with complex ideas ...in refreshingly simple language...I would highly recommend it.’ Brendan Hoban, Western People, Feb21 -- Brendan Hoban * Western People *How should we respond ethically to these developments? All this, and much more, is explored by the author...' -- Nigel Waugh * Church Review *The book ranges widely... it is not limited to truckers and retailers, but also to high-end economic work, from factories in Bangladesh to stockbrokers in New York... an engaging introduction to the subject’ The Irish Catholic Apr21 -- Ruadhán Jones * The Irish Catholic *'...this is the best introduction to these questions yet written for a popular Irish audience. It is accessible to a reader who is coming to these topics completely fresh. McDonagh will deepen your understanding and prompt you to question your assumptions.' -- Kevin Hargaden * Jesuit Centre for Faith & Justice *‘McDonagh draws on the experience of the COVID-19 pandemic to spotlight how people might react to being out of work on a mass scale. [This book] highlights the need to address the potential repercussions of these developing technologies.’ -- Sarah MacDonald * National Catholic Reporter *‘...timely, accessible, well-researched and thought-provoking: this is a thoroughly readable and enjoyable book. It addresses some of the key challenges facing twenty-first century society and should be essential reading for anyone in public life or in education.’ -- Pat O'Mahony * Doctrine & Life *‘...intelligent, critically reflective, and honest. McDonagh writes with clarity and charity. The book is a pleasure to read.’ -- Christopher West * Search Journal *‘clear and accessible, it is an engaging read and a good introduction to the topic for the lay person’ Studies: An Irish Quarterly Review -- Margaret McGaley * Studies: An Irish Quarterly Review *‘...highly informative, provocative and challenging...a must-read, especially for those in leadership roles. A book critical for our times’ The Furrow Oct 21 -- Suzanne Mulligan * The Furrow *

Read more less

Book SynopsisMachining, as a reliable manufacturing process, still offers unmatched capabilities in producing high quality three-dimensional parts from metals, polymers, ceramics, wood and composites. Advances in computational modeling and optimization methods enabled researchers to develop cost effective and high throughput modern machining processes. This book aims to provide recent advances intelligent machining for modern manufacturing engineering. It includes six chapters that provide basic fundamentals, modern machining processes, analytical and mechanistic modeling approaches, finite element modeling and systems based modeling, recent optimization methods and case studies.

Read more less

Book SynopsisThe new digital media offers us an unprecedented memory capacity, an ubiquitous communication channel and a growing computing power. How can we exploit this medium to augment our personal and social cognitive processes at the service of human development? Combining a deep knowledge of humanities and social sciences as well as a real familiarity with computer science issues, this book explains the collaborative construction of a global hypercortex coordinated by a computable metalanguage. By recognizing fully the symbolic and social nature of human cognition, we could transform our current opaque global brain into a reflexive collective intelligence.Table of ContentsAcknowledgements xv Chapter 1. General Introduction 1 1.1. The vision: to enhance cognitive processes 2 1.2. A transdisciplinary intellectual adventure 5 1.3. The result: toward hypercortical cognition 27 1.4. General plan of this book 35 PART 1. THE PHILOSOPHY OF INFORMATION 37 Chapter 2. The Nature of Information 41 2.1. Orientation 41 2.2. The information paradigm 45 2.3. Layers of encoding 56 2.4. Evolution in information nature 66 2.5. The unity of nature 69 Chapter 3. Symbolic Cognition 75 3.1. Delimitation of the field of symbolic cognition76 3.2. The secondary reflexivity of symbolic cognition 78 3.3. Symbolic power and its manifestations 80 3.4. The reciprocal enveloping of the phenomenal world and semantic world 82 3.5. The open intelligence of culture 84 3.6. Differences between animal and human collective intelligence 85 Chapter 4. Creative Conversation 89 4.1. Beyond “collective stupidity” 89 4.2. Reflexive explication and sharing of knowledge 92 4.3. The symbolic medium of creative conversation 103 Chapter 5. Toward an Epistemological Transformation of the Human Sciences 113 5.1. The stakes of human development 113 5.2. Critique of the human sciences 120 5.3. The threefold renewal of the human sciences 125 5.4. The Ouroboros 133 Chapter 6. The Information Economy 135 6.1. The symbiosis of knowledge capital and cognitive labor 136 6.2. Toward scientific self-management of collective intelligence 140 6.3. Flows of symbolic energy 144 6.4. Ecosystems of ideas and the semantic information economy 148 6.5. The semantic information economy in the digital medium 154 PART 2. MODELING COGNITION 159 Chapter 7. Introduction to the Scientific Knowledge of the Mind 161 7.1. Research program 161 7.2. The mind in nature 165 7.3. The three symbolic functions of the cortex 171 7.4. The IEML model of symbolic cognition. 176 7.5. The architecture of the Hypercortex 184 7.6. Overview: toward a reflexive collective intelligence 187 Chapter 8. The Computer Science Perspective: Toward a Reflexive Intelligence 189 8.1. Augmented collective intelligence 189 8.2. The purpose of automatic manipulation of symbols: cognitive modeling and self-knowledge 194 8.3. The means of automatic manipulation of symbols: beyond probabilities and logic 202 Chapter 9. General Presentation of the IEML Semantic Sphere 207 9.1. Ideas 208 9.2. Concepts 213 9.3. Unity and calculability 217 9.4. Symmetry 220 9.5. Internal coherence 225 9.6. Inexhaustible complexity 230 Chapter 10. The IEML Metalanguage 235 10.1. The problem of encoding concepts 235 10.2. Text units 238 10.3. Circuits of meaning 241 10.4. Between text and circuits 244 Chapter 11. The IEML Semantic Machine 253 11.1. Overview of the functions involved in symbolic cognition 253 11.2. Requirements for the construction of the IEML semantic machine 258 11.3. The IEML textual machine (S) 261 11.4. The STAR (Semantic Tool for Augmented Reasoning) linguistic engine (B) 264 11.5. The conceptual machine (T) 267 11.6. Conclusion 270 Chapter 12. The Hypercortex 275 12.1. The role of media and symbolic systems in cognition 275 12.2. The digital medium 277 12.3. The evolution of the layers of addressing in the digital medium 284 12.4. Between the Cortex and the Hypercortex 289 12.5. Toward an observatory of collective intelligence 291 12.6. Conclusion: the computability and interoperability of semantic and hermeneutic functions 296 Chapter 13. Hermeneutic Memory 299 13.1. Toward a semantic organization of memory 299 13.2. The layers of complexity of memory 302 13.3. Radical hermeneutics 304 13.4. The hermeneutics of information 308 13.5. The hermeneutics of knowledge 312 13.6. Wisdom 317 13.7. Collective interpretation games 318 Chapter 14. The Perspective of the Humanities: Toward Explicit Knowledge 323 14.1. Context 323 14.2. Methodology: the digital humanities 327 14.3. Epistemology: explicating symbolic cognition 331 Chapter 15. Observing Collective Intelligence 341 15.1. The semantic sphere as a mirror of concepts 341 15.2. The structure of the cognitive image 346 15.3. The two eyes of reflexive observation 350 Bibliography 353 Index 377

Read more less

Book SynopsisThis book describes the potentialities of metaheuristics for solving production scheduling problems and the relationship between these two fields. For the past several years, there has been an increasing interest in using metaheuristic methods to solve scheduling problems. The main reasons for this are that such problems are generally hard to solve to optimality, as well as the fact that metaheuristics provide very good solutions in a reasonable time. The first part of the book presents eight applications of metaheuristics for solving various mono-objective scheduling problems. The second part is itself split into two, the first section being devoted to five multi-objective problems to which metaheuristics are adapted, while the second tackles various transportation problems related to the organization of production systems. Many real-world applications are presented by the authors, making this an invaluable resource for researchers and students in engineering, economics, mathematics and computer science. Contents 1. An Estimation of Distribution Algorithm for Solving Flow Shop Scheduling Problems with Sequence-dependent Family Setup Times, Mansour Eddaly, Bassem Jarboui, Radhouan Bouabda, Patrick Siarry and Abdelwaheb Rebaï. 2. Genetic Algorithms for Solving Flexible Job Shop Scheduling Problems, Imed Kacem. 3. A Hybrid GRASP-Differential Evolution Algorithm for Solving Flow Shop Scheduling Problems with No-Wait Constraints, Hanen Akrout, Bassem Jarboui, Patrick Siarry and Abdelwaheb Rebaï. 4. A Comparison of Local Search Metaheuristics for a Hierarchical Flow Shop Optimization Problem with Time Lags, Emna Dhouib, Jacques Teghem, Daniel Tuyttens and Taïcir Loukil. 5. Neutrality in Flow Shop Scheduling Problems: Landscape Structure and Local Search, Marie-Eléonore Marmion. 6. Evolutionary Metaheuristic Based on Genetic Algorithm: Application to Hybrid Flow Shop Problem with Availability Constraints, Nadia Chaaben, Racem Mellouli and Faouzi Masmoudi. 7. Models and Methods in Graph Coloration for Various Production Problems, Nicolas Zufferey. 8. Mathematical Programming and Heuristics for Scheduling Problems with Early and Tardy Penalties, Mustapha Ratli, Rachid Benmansour, Rita Macedo, Saïd Hanafi, Christophe Wilbaut. 9. Metaheuristics for Biobjective Flow Shop Scheduling, Matthieu Basseur and Arnaud Liefooghe. 10. Pareto Solution Strategies for the Industrial Car Sequencing Problem, Caroline Gagné, Arnaud Zinflou and Marc Gravel. 11. Multi-Objective Metaheuristics for the Joint Scheduling of Production and Maintenance, Ali Berrichi and Farouk Yalaoui. 12. Optimization via a Genetic Algorithm Parametrizing the AHP Method for Multicriteria Workshop Scheduling, Fouzia Ounnar, Patrick Pujo and Afef Denguir. 13. A Multicriteria Genetic Algorithm for the Resource-constrained Task Scheduling Problem, Olfa Dridi, Saoussen Krichen and Adel Guitouni. 14. Metaheuristics for the Solution of Vehicle Routing Problems in a Dynamic Context, Tienté Hsu, Gilles Gonçalves and Rémy Dupas. 15. Combination of a Metaheuristic and a Simulation Model for the Scheduling of Resource-constrained Transport Activities, Virginie André, Nathalie Grangeon and Sylvie Norre. 16. Vehicle Routing Problems with Scheduling Constraints, Rahma Lahyani, Frédéric Semet and Benoît Trouillet. 17. Metaheuristics for Job Shop Scheduling with Transportation, Qiao Zhang, Hervé Manier, Marie-Ange Manier. About the Authors Bassem Jarboui is Professor at the University of Sfax, Tunisia. Patrick Siarry is Professor at the Laboratoire Images, Signaux et Systèmes Intelligents (LISSI), University of Paris-Est Créteil, France. Jacques Teghem is Professor at the University of Mons, Belgium.Table of ContentsIntroduction and Presentation xv Bassem JARBOUI, Patrick SIARRY and Jacques TEGHEM Chapter 1. An Estimation of Distribution Algorithm for Solving Flow Shop Scheduling Problems with Sequence-dependent Family Setup Times 1 Mansour EDDALY, Bassem JARBOUI, Radhouan BOUABDA, Patrick SIARRY and Abdelwaheb REBAÏ 1.1. Introduction 1 1.2. Mathematical formulation 3 1.3. Estimation of distribution algorithms 5 1.3.1. Estimation of distribution algorithms proposed in the literature 6 1.4. The proposed estimation of distribution algorithm 8 1.4.1. Encoding scheme and initial population 8 1.4.2. Selection 9 1.4.3. Probability estimation 9 1.5. Iterated local search algorithm 10 1.6. Experimental results 11 1.7. Conclusion 15 1.8. Bibliography 15 Chapter 2. Genetic Algorithms for Solving Flexible Job Shop Scheduling Problems 19 Imed KACEM 2.1. Introduction 19 2.2. Flexible job shop scheduling problems 19 2.3. Genetic algorithms for some related sub-problems 25 2.4. Genetic algorithms for the flexible job shop problem 31 2.4.1. Codings 31 2.4.2. Mutation operators 34 2.4.3. Crossover operators 38 2.5. Comparison of codings 42 2.6. Conclusion 43 2.7. Bibliography 43 Chapter 3. A Hybrid GRASP-Differential Evolution Algorithm for Solving Flow Shop Scheduling Problems with No-Wait Constraints 45 Hanen AKROUT, Bassem JARBOUI, Patrick SIARRY and Abdelwaheb REBAÏ 3.1. Introduction 45 3.2. Overview of the literature 47 3.2.1. Single-solution metaheuristics 47 3.2.2. Population-based metaheuristics 49 3.2.3. Hybrid approaches 49 3.3. Description of the problem 50 3.4. GRASP 52 3.5. Differential evolution 53 3.6. Iterative local search 55 3.7. Overview of the NEW-GRASP-DE algorithm 55 3.7.1. Constructive phase 56 3.7.2. Improvement phase 57 3.8. Experimental results 57 3.8.1. Experimental results for the Reeves and Heller instances 58 3.8.2. Experimental results for the Taillard instances 60 3.9. Conclusion 62 3.10. Bibliography 64 Chapter 4. A Comparison of Local Search Metaheuristics for a Hierarchical Flow Shop Optimization Problem with Time Lags 69 Emna DHOUIB, Jacques TEGHEM, Daniel TUYTTENS and Taïcir LOUKIL 4.1. Introduction 69 4.2. Description of the problem 70 4.2.1. Flowshop with time lags 70 4.2.2. A bicriteria hierarchical flow shop problem 71 4.3. The proposed metaheuristics 73 4.3.1. A simulated annealing metaheuristics 74 4.3.2. The GRASP metaheuristics 77 4.4. Tests 82 4.4.1. Generated instances 82 4.4.2. Comparison of the results 83 4.5. Conclusion 94 4.6. Bibliography 94 Chapter 5. Neutrality in Flow Shop Scheduling Problems: Landscape Structure and Local Search 97 Marie-Eléonore MARMION 5.1. Introduction 97 5.2. Neutrality in a combinatorial optimization problem 98 5.2.1. Landscape in a combinatorial optimization problem 99 5.2.2. Neutrality and landscape 102 5.3. Study of neutrality in the flow shop problem 106 5.3.1. Neutral degree 106 5.3.2. Structure of the neutral landscape 108 5.4. Local search exploiting neutrality to solve the flow shop problem 112 5.4.1. Neutrality-based iterated local search 113 5.4.2. NILS on the flow shop problem 116 5.5. Conclusion 122 5.6. Bibliography 123 Chapter 6. Evolutionary Metaheuristic Based on Genetic Algorithm: Application to Hybrid Flow Shop Problem with Availability Constraints 127 Nadia CHAABEN, Racem MELLOULI and Faouzi MASMOUDI 6.1. Introduction 127 6.2. Overview of the literature 128 6.3. Overview of the problem and notations used 131 6.4. Mathematical formulations 133 6.4.1. First formulation (MILP1) 133 6.4.2. Second formulation (MILP2) 135 6.4.3. Third formulation (MILP3) 137 6.5. A genetic algorithm: model and methodology 139 6.5.1. Coding used for our algorithm 139 6.5.2. Generating the initial population 140 6.5.3. Selection operator 142 6.5.4. Crossover operator 142 6.5.5. Mutation operator 144 6.5.6. Insertion operator 144 6.5.7. Evaluation function: fitness 144 6.5.8. Stop criterion 145 6.6. Verification and validation of the genetic algorithm 145 6.6.1. Description of benchmarks 145 6.6.2. Tests and results 146 6.7. Conclusion 148 6.8. Bibliography 148 Chapter 7. Models and Methods in Graph Coloration for Various Production Problems 153 Nicolas ZUFFEREY 7.1. Introduction 153 7.2. Minimizing the makespan 155 7.2.1. Tabu algorithm 155 7.2.2. Hybrid genetic algorithm 157 7.2.3. Methods prior to GH 158 7.2.4. Extensions 159 7.3. Maximizing the number of completed tasks 160 7.3.1. Tabu algorithm 161 7.3.2. The ant colony algorithm 162 7.3.3. Extension of the problem 164 7.4. Precedence constraints 165 7.4.1. Tabu algorithm 168 7.4.2. Variable neighborhood search method 169 7.5. Incompatibility costs 171 7.5.1. Tabu algorithm 173 7.5.2. Adaptive memory method 175 7.5.3. Variations of the problem 177 7.6. Conclusion 178 7.7. Bibliography 179 Chapter 8. Mathematical Programming and Heuristics for Scheduling Problems with Early and Tardy Penalties 183 Mustapha RATLI, Rachid BENMANSOUR, Rita MACEDO, Saïd HANAFI, Christophe WILBAUT 8.1. Introduction 183 8.2. Properties and particular cases 185 8.3. Mathematical models 188 8.3.1. Linear models with precedence variables 188 8.3.2. Linear models with position variables 192 8.3.3. Linear models with time-indexed variables 194 8.3.4. Network flow models 197 8.3.5. Quadratic models 197 8.3.6. A comparative study 199 8.4. Heuristics 203 8.4.1. Properties 207 8.4.2. Evaluation 209 8.5. Metaheuristics 211 8.6. Conclusion 217 8.7. Acknowledgments 218 8.8. Bibliography 218 Chapter 9. Metaheuristics for Biobjective Flow Shop Scheduling 225 Matthieu BASSEUR and Arnaud LIEFOOGHE 9.1. Introduction 225 9.2. Metaheuristics for multiobjective combinatorial optimization 226 9.2.1. Main concepts 227 9.2.2. Some methods 229 9.2.3. Performance analysis 232 9.2.4. Software and implementation 237 9.3. Multiobjective flow shop scheduling problems 238 9.3.1. Flow shop problems 239 9.3.2. Permutation flow shop with due dates 240 9.3.3. Different objective functions 241 9.3.4. Sets of data 241 9.3.5. Analysis of correlations between objectives functions 242 9.4. Application to the biobjective flow shop 243 9.4.1. Model 244 9.4.2. Solution methods 246 9.4.3. Experimental analysis 246 9.5. Conclusion 249 9.6. Bibliography 250 Chapter 10. Pareto Solution Strategies for the Industrial Car Sequencing Problem 253 Caroline GAGNÉ, Arnaud ZINFLOU and Marc GRAVEL 10.1. Introduction 253 10.2. Industrial car sequencing problem 255 10.3. Pareto strategies for solving the CSP 260 10.3.1. PMSMO 260 10.3.2. GISMOO 264 10.4. Numerical experiments 268 10.4.1. Test sets 269 10.4.2. Performance metrics 270 10.5. Results and discussion 271 10.6. Conclusion 279 10.7. Bibliography 280 Chapter 11. Multi-Objective Metaheuristics for the Joint Scheduling of Production and Maintenance 283 Ali BERRICHI and Farouk YALAOUI 11.1. Introduction 283 11.2. State of the art on the joint problem 285 11.3. Integrated modeling of the joint problem 287 11.4. Concepts of multi-objective optimization 291 11.5. The particle swarm optimization method 292 11.6. Implementation of MOPSO algorithms 294 11.6.1. Representation and construction of the solutions 294 11.6.2. Solution Evaluation 295 11.6.3. The proposed MOPSO algorithms 298 11.6.4. Updating the velocities and positions 299 11.6.5. Hybridization with local searches 300 11.7. Experimental results 302 11.7.1. Choice of test problems and configurations 302 11.7.2. Experiments and analysis of the results 303 11.8. Conclusion 310 11.9. Bibliography 311 Chapter 12. Optimization via a Genetic Algorithm Parametrizing the AHP Method for Multicriteria Workshop Scheduling 315 Fouzia OUNNAR, Patrick PUJO and Afef DENGUIR 12.1. Introduction 315 12.2. Methods for solving multicriteria scheduling 316 12.2.1. Optimization methods 316 12.2.2. Multicriteria decision aid methods 318 12.2.3. Choice of the multicriteria decision aid method 319 12.3. Presentation of the AHP method 320 12.3.1. Phase 1: configuration 320 12.3.2. Phase 2: exploitation 321 12.4. Evaluation of metaheuristics for the configuration of AHP 322 12.4.1. Local search methods 323 12.4.2. Population-based methods 324 12.4.3. Advanced metaheuristics 326 12.5. Choice of metaheuristic 326 12.5.1. Justification of the choice of genetic algorithms 326 12.5.2. Genetic algorithms 328 12.6. AHP optimization by a genetic algorithm 330 12.6.1. Phase 0: configuration of the structure of the problem 331 12.6.2. Phase 1: preparation for automatic configuration 332 12.6.3. Phase 2: automatic configuration 334 12.6.4. Phase 3: preparation of the exploitation phase 335 12.7. Evaluation of G-AHP 336 12.7.1. Analysis of the behavior of G-AHP 336 12.7.2. Analysis of the results obtained by G-AHP 342 12.8. Conclusions 343 12.9. Bibliography 344 Chapter 13. A Multicriteria Genetic Algorithm for the Resource-constrained Task Scheduling Problem 349 Olfa DRIDI, Saoussen KRICHEN and Adel GUITOUNI 13.1. Introduction 349 13.2. Description and formulation of the problem 350 13.3. Literature review 353 13.3.1. Exact methods 354 13.3.2. Approximate methods 355 13.4. A multicriteria genetic algorithm for the MMSAP 356 13.4.1. Encoding variables 357 13.4.2. Genetic operators 358 13.4.3. Parameter settings 359 13.4.4. The GA 360 13.5. Experimental study 361 13.5.1. Diversification of the approximation set based on the diversity indicators 364 13.6. Conclusion 369 13.7. Bibliography 369 Chapter 14. Metaheuristics for the Solution of Vehicle Routing Problems in a Dynamic Context 373 Tienté HSU, Gilles GONÇALVES and Rémy DUPAS 14.1. Introduction 373 14.2. Dynamic vehicle route management 375 14.2.1. The vehicle routing problem with time windows 377 14.3. Platform for the solution of the DVRPTW 382 14.3.1. Encoding a chromosome 384 14.4. Treating uncertainties in the orders 386 14.5. Treatment of traffic information 392 14.6. Conclusion 397 14.7. Bibliography 398 Chapter 15. Combination of a Metaheuristic and a Simulation Model for the Scheduling of Resource-constrained Transport Activities 401 Virginie ANDRÉ, Nathalie GRANGEON and Sylvie NORRE 15.1. Knowledge model 403 15.1.1. Fixed resources and mobile resources 403 15.1.2. Modelling the activities in steps 404 15.1.3. The problem to be solved 406 15.1.4. Illustrative example 407 15.2. Solution procedure 410 15.3. Proposed approach 413 15.3.1. Metaheuristics 414 15.3.2. Simulation model 421 15.4. Implementation and results 422 15.4.1. Impact on the work mode 423 15.4.2. Results of the set of modifications to the teaching hospital 425 15.4.3. Preliminary study of the choice of shifts 428 15.5. Conclusion 430 15.6. Bibliography 431 Chapter 16. Vehicle Routing Problems with Scheduling Constraints 433 Rahma LAHYANI, Frédéric SEMET and Benoît TROUILLET 16.1. Introduction 433 16.2. Definition, complexity and classification 435 16.2.1. Definition and complexity 435 16.2.2. Classification 436 16.3. Time-constrained vehicle routing problems 438 16.3.1. Vehicle routing problems with time windows 438 16.3.2. Period vehicle routing problems 441 16.3.3. Vehicle routing problem with cross-docking 443 16.4. Vehicle routing problems with resource availability constraints 448 16.4.1. Multi-trip vehicle routing problem 448 16.4.2. Vehicle routing problem with crew scheduling 450 16.5. Conclusion 452 16.6. Bibliography 453 Chapter 17. Metaheuristics for Job Shop Scheduling with Transportation 465 Qiao ZHANG, Hervé MANIER, Marie-Ange MANIER 17.1. General flexible job shop scheduling problems 466 17.2. State of the art on job shop scheduling with transportation resources 468 17.3. GTSB procedure 474 17.3.1. A hybrid metaheuristic algorithm for the GFJSSP 474 17.3.2. Tests and results 480 17.3.3. Conclusion for GTSB 489 17.4. Conclusion 491 17.5. Bibliography 491 List of Authors 495 Index 499

Read more less