The commercial success of a game is often dependent upon the quality of the AI, yet the engineering of AI is often begun late in the development process and is frequently misunderstood. In this book, Ian Millington brings extensive professional experience to the problem of improving the quality of AI in games. A game developer since 1987, he was founder of Mindlathe Ltd., at the time the largest specialist AI company in gaming. Ian shows how to think about AI as an integral part of game play. He describes numerous examples from real games and explores the underlying ideas through detailed case studies. He goes further to introduce many techniques little used by developers today. The book's CD-ROM contains a library of C++ source code and demonstration programs, and provides access to a website with a complete commercial source code library of AI algorithms and techniques. * A comprehensive, professional tutorial and reference to implement true AI in games.* Walks through the entire development process from beginning to end.* Includes over 100 pseudo code examples of techniques used in commercial games, case studies for all major genres, a CD-ROM and companion website with extensive C++ source code implementations for Windows, and source code libraries for Linux and OS X available through the website.

    He solidified his standing as the nation's foremost political forecaster with his near perfect prediction of the 2012 election. Silver is the founder and editor in chief of FiveThirtyEight.com. Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society, because most of us have a poor understanding of probability and uncertainty. Both experts and laypeople mistake more confident predictions for more accurate ones. But overconfidence is often the reason for failure. If our appreciation of uncertainty improves, our predictions can get better too. This is the "prediction paradox": The more humility we have about our ability to make predictions, the more successful we can be in planning for the future.In keeping with his own aim to seek truth from data, Silver visits the most successful forecasters in a range of areas, from hurricanes to baseball, from the poker table to the stock market, from Capitol Hill to the NBA. He explains and evaluates how these forecasters think and what bonds they share. What lies behind their success? Are they good-or just lucky? What patterns have they unraveled? And are their forecasts really right? He explores unanticipated commonalities and exposes unexpected juxtapositions. And sometimes, it is not so much how good a prediction is in an absolute sense that matters but how good it is relative to the competition. In other cases, prediction is still a very rudimentary-and dangerous-science.Silver observes that the most accurate forecasters tend to have a superior command of probability, and they tend to be both humble and hardworking. They distinguish the predictable from the unpredictable, and they notice a thousand little details that lead them closer to the truth. Because of their appreciation of probability, they can distinguish the signal from the noise.

    Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy.Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.

    Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs. No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required. 0201157675B07092001

    Each self-contained chapter consists of three sections. The first describes the statistic, including how it is used and what information it provides. The second section reviews how it works, how to calculate the formula, the strengths and weaknesses of the technique, and the conditions needed for its use. The final section provides examples that use and interpret the statistic. A glossary of terms and symbols is also included.New features in the second edition include:an interactive CD with PowerPoint presentations and problems for each chapter including an overview of the problem's solution; new chapters on basic research concepts including sampling, definitions of different types of variables, and basic research designs and one on nonparametric statistics; more graphs and more precise descriptions of each statistic; and a discussion of confidence intervals.This brief paperback is an ideal supplement for statistics, research methods, courses that use statistics, or as a reference tool to refresh one's memory about key concepts. The actual research examples are from psychology, education, and other social and behavioral sciences.Materials formerly available with this book on CD-ROM are now available for download from our website www.psypress.com. Go to the book's page and look for the 'Download' link in the right-hand column.

    By presenting actual experiences and analyzing them as they are described, the author conveys the developmental thought processes employed and shows a style of thinking that leads to successful results is something that can be learned. Along with spectacular successes, the author also conveys how failures contributed to shaping the thought processes. Provides the reader with a style of thinking that will enhance a person's ability to function as a problem-solver of complex technical issues. Consists of a collection of stories about the author's participation in significant discoveries, relating how those discoveries came about and, most importantly, provides analysis about the thought processes and reasoning that took place as the author and his associates progressed through engineering problems.

    However, data structures for these languages do not always translate well to functional languages such as Standard ML, Haskell, or Scheme. This book describes data structures from the point of view of functional languages, with examples, and presents design techniques that allow programmers to develop their own functional data structures. The author includes both classical data structures, such as red-black trees and binomial queues, and a host of new data structures developed exclusively for functional languages. All source code is given in Standard ML and Haskell, and most of the programs are easily adaptable to other functional languages. This handy reference for professional programmers working with functional languages can also be used as a tutorial or for self-study.

    This is the currently used textbook for "Probabilistic Systems Analysis," an introductory probability course at the Massachusetts Institute of Technology, attended by a large number of undergraduate and graduate students. The book covers the fundamentals of probability theory (probabilistic models, discrete and continuous random variables, multiple random variables, and limit theorems), which are typically part of a first course on the subject. It also contains, a number of more advanced topics, from which an instructor can choose to match the goals of a particular course. These topics include transforms, sums of random variables, least squares estimation, the bivariate normal distribution, and a fairly detailed introduction to Bernoulli, Poisson, and Markov processes. The book strikes a balance between simplicity in exposition and sophistication in analytical reasoning. Some of the more mathematically rigorous analysis has been just intuitively explained in the text, but is developed in detail (at the level of advanced calculus) in the numerous solved theoretical problems. The book has been widely adopted for classroom use in introductory probability courses within the USA and abroad.

    You'll learn how to create responsive, data-driven websites with PHP, MySQL, and JavaScript, regardless of whether you already know how to program. Discover how the powerful combination of PHP and MySQL provides an easy way to build modern websites complete with dynamic data and user interaction. You'll also learn how to add JavaScript to create rich Internet applications and websites.Learning PHP, MySQL, and JavaScript explains each technology separately, shows you how to combine them, and introduces valuable web programming concepts, including objects, XHTML, cookies, and session management. You'll practice what you've learned with review questions in each chapter, and find a sample social networking platform built with the elements introduced in this book. This book will help you:-Understand PHP essentials and the basics of object-oriented programming-Master MySQL, from database structure to complex queries-Create web pages with PHP and MySQL by integrating forms and other HTML features-Learn about JavaScript, from functions and event handling to accessing the Document Object Model-Use libraries and packages, including the Smarty web template system, PEAR program repository, and the Yahoo! User Interface Library -Make Ajax calls and turn your website into a highly dynamic environment-Upload and manipulate files and images, validate user input, and secure your applications

    This authoritative book scrapes away these bad features to reveal a subset of JavaScript that's more reliable, readable, and maintainable than the language as a whole--a subset you can use to create truly extensible and efficient code.Considered the JavaScript expert by many people in the development community, author Douglas Crockford identifies the abundance of good ideas that make JavaScript an outstanding object-oriented programming language-ideas such as functions, loose typing, dynamic objects, and an expressive object literal notation. Unfortunately, these good ideas are mixed in with bad and downright awful ideas, like a programming model based on global variables.When Java applets failed, JavaScript became the language of the Web by default, making its popularity almost completely independent of its qualities as a programming language. In JavaScript: The Good Parts, Crockford finally digs through the steaming pile of good intentions and blunders to give you a detailed look at all the genuinely elegant parts of JavaScript, including:SyntaxObjectsFunctionsInheritanceArraysRegular expressionsMethodsStyleBeautiful featuresThe real beauty? As you move ahead with the subset of JavaScript that this book presents, you'll also sidestep the need to unlearn all the bad parts. Of course, if you want to find out more about the bad parts and how to use them badly, simply consult any other JavaScript book.With JavaScript: The Good Parts, you'll discover a beautiful, elegant, lightweight and highly expressive language that lets you create effective code, whether you're managing object libraries or just trying to get Ajax to run fast. If you develop sites or applications for the Web, this book is an absolute must.

    Updated to cover Internet Explorer 7, Microsoft's vastly improved browser, this new edition includes content on positioning, lists and generated content, table layout, user interface, paged media, and more.Simply put, Cascading Style Sheets (CSS) is a way to separate a document's structure from its presentation. The benefits of this can be quite profound: CSS allows a much richer document appearance than HTML and also saves time -- you can create or change the appearance of an entire document in just one place; and its compact file size makes web pages load quickly.CSS: The Definitive Guide, 3rd Edition, provides you with a comprehensive guide to CSS implementation, along with a thorough review of all aspects of CSS 2.1. Updated to cover Internet Explorer 7, Microsoft's vastly improved browser, this new edition includes content on positioning, lists and generated content, table layout, user interface, paged media, and more. Author Eric Meyer tackles the subject with passion, exploring in detail each individual CSS property and how it interacts with other properties. You'll not only learn how to avoid common mistakes in interpretation, you also will benefit from the depth and breadth of his experience and his clear and honest style. This is the complete sourcebook on CSS.The 3rd edition contains:Updates to reflect changes in the latest draft version of CSS 2.1Browser notes updated to reflect changes between IE6 and IE7Advanced selectors supported in IE7 and other major browsers includedA new round of technical edits by a fresh set of editorsClarifications and corrected errata, including updated URLs ofreferenced online resources

    Scholars and students need to be able to analyze, design and curate information into useful tools of communication, insight and understanding. This book is the starting point in learning the process and skills of data visualization, teaching the concepts and skills of how to present data and inspiring effective visual design. Benefits of this book: A flexible step-by-step journey that equips you to achieve great data visualization.A curated collection of classic and contemporary examples, giving illustrations of good and bad practice Examples on every page to give creative inspiration Illustrations of good and bad practice show you how to critically evaluate and improve your own work Advice and experience from the best designers in the field Loads of online practical help, checklists, case studies and exercises make this the most comprehensive text available

    In Turing’s Cathedral, George Dyson focuses on a small group of men and women, led by John von Neumann at the Institute for Advanced Study in Princeton, New Jersey, who built one of the first computers to realize Alan Turing’s vision of a Universal Machine. Their work would break the distinction between numbers that mean things and numbers that do things—and our universe would never be the same. Using five kilobytes of memory (the amount allocated to displaying the cursor on a computer desktop of today), they achieved unprecedented success in both weather prediction and nuclear weapons design, while tackling, in their spare time, problems ranging from the evolution of viruses to the evolution of stars. Dyson’s account, both historic and prophetic, sheds important new light on how the digital universe exploded in the aftermath of World War II. The proliferation of both codes and machines was paralleled by two historic developments: the decoding of self-replicating sequences in biology and the invention of the hydrogen bomb. It’s no coincidence that the most destructive and the most constructive of human inventions appeared at exactly the same time.  How did code take over the world? In retracing how Alan Turing’s one-dimensional model became John von Neumann’s two-dimensional implementation, Turing’s Cathedral offers a series of provocative suggestions as to where the digital universe, now fully three-dimensional, may be heading next.

    You'll learn how to use Haskell in a variety of practical ways, from short scripts to large and demanding applications. Real World Haskell takes you through the basics of functional programming at a brisk pace, and then helps you increase your understanding of Haskell in real-world issues like I/O, performance, dealing with data, concurrency, and more as you move through each chapter. With this book, you will:Understand the differences between procedural and functional programming Learn the features of Haskell, and how to use it to develop useful programs Interact with filesystems, databases, and network services Write solid code with automated tests, code coverage, and error handling Harness the power of multicore systems via concurrent and parallel programming You'll find plenty of hands-on exercises, along with examples of real Haskell programs that you can modify, compile, and run. Whether or not you've used a functional language before, if you want to understand why Haskell is coming into its own as a practical language in so many major organizations, Real World Haskell is the best place to start.

    The authors also include examples covering a variety of different integration technologies, such as JMS, MSMQ, TIBCO ActiveEnterprise, Microsoft BizTalk, SOAP, and XSL. A case study describing a bond trading system illustrates the patterns in practice, and the book offers a look at emerging standards, as well as insights into what the future of enterprise integration might hold. This book provides a consistent vocabulary and visual notation framework to describe large-scale integration solutions across many technologies. It also explores in detail the advantages and limitations of asynchronous messaging architectures. The authors present practical advice on designing code that connects an application to a messaging system, and provide extensive information to help you determine when to send a message, how to route it to the proper destination, and how to monitor the health of a messaging system. If you want to know how to manage, monitor, and maintain a messaging system once it is in use, get this book.