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Books : Computers & Internet : Programming : Algorithms : Fuzzy Logic
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Advances in 3D visualization and physics-based simulation technology make it possible for game developers to create compelling, visually immersive gaming environments that were only dreamed of years ago. But today's game players have grown in sophistication along with the games they play. It's no longer enough to wow your players with dazzling graphics; the next step in creating even more immersive games is improved artificial intelligence, or AI. Fortunately, advanced AI game techniques are within the grasp of every game developer--not just those who dedicate their careers to AI. If you're new to game programming or if you're an experienced game programmer who needs to get up to speed quickly on AI techniques, you'll find AI for Game Developers to be the perfect starting point for understanding and applying AI techniques to your games. Written for the novice AI programmer, AI for Game Developers introduces you to techniques such as finite state machines, fuzzy logic, neural networks, and many others, in straightforward, easy-to-understand language, supported with code samples throughout the entire book (written in C/C++). From basic techniques such as chasing and evading, pattern movement, and flocking to genetic algorithms, the book presents a mix of deterministic (traditional) and non-deterministic (newer) AI techniques aimed squarely at beginners AI developers. Other topics covered in the book include:- Potential function based movements: a technique that handles chasing, evading swarming, and collision avoidance simultaneously
- Basic pathfinding and waypoints, including an entire chapter devoted to the A* pathfinding algorithm
- AI scripting
- Rule-based AI: learn about variants other than fuzzy logic and finite state machines
- Basic probability
- Bayesian techniques
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"Fuzzy logic" is a way to program computers so that they can mimic the imprecise way that humans make decisions. This important book traces the dramatic story of Lofti Zadeh, the Iranian-American professor who developed this concept, and his struggle to sell it to the American academic and business communities.
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A First Course in Fuzzy Logic, Third Edition continues to provide the ideal introduction to the theory and applications of fuzzy logic. This best-selling text provides a firm mathematical basis for the calculus of fuzzy concepts necessary for designing intelligent systems and a solid background for readers to pursue further studies and real-world applications. New in the Third Edition: · A section on type-2 fuzzy sets - a topic that has received much attention in the past few years · Additional material on copulas and t-norms · More discussions on generalized modus ponens and the compositional rule of inference · Complete revision to the chapter on possibility theory · Significant expansion of the chapter on fuzzy integrals · Many new exercises With its comprehensive updates, this new edition presents all the background necessary for students and professionals to begin using fuzzy logic in its many-and rapidly growing- applications in computer science, mathematics, statistics, and engineering.
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A washing machine that gauges each load to determine how much soap to use? An air conditioner that constantly adjusts cooling strength based on room temperature? These "smart" products are possible with the advent of "fuzzy logic, " the principle that's revolutionizing science. Now, its chief proponent presents a brilliant, popular account of the field called the "new chaos.".
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An interdisciplinary framework for learning methodologies-covering statistics, neural networks, and fuzzy logic This book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied-showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples, Learning from Data:
* Relates statistical formulation with the latest methodologies used in artificial neural networks, fuzzy systems, and wavelets
* Features consistent terminology, chapter summaries, and practical research tips
* Emphasizes the conceptual framework provided by Statistical Learning Theory (VC-theory) rather than its commonly practiced mathematical aspects
* Provides a detailed description of the new learning methodology called Support Vector Machines (SVM)
This invaluable text/reference accommodates both beginning and advanced graduate students in engineering, computer science, and statistics. It is also indispensable for researchers and practitioners in these areas who must understand the principles and methods for learning dependencies from data.
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The study of nonlinear dynamical systems has advanced tremendously in the last 15 years, making a big impact on science and technology. This book provides all the techniques and methods used in nonlinear dynamics. The concepts and underlying mathematics are discussed in detail.The numerical and symbolic methods are implemented in C++, SymbolicC++ and Java. Object-oriented techniques are also applied. The book contains more than 100 ready-to-run programs.
The text has also been designed for a one-year course at both the junior and senior levels in nonlinear dynamics. The topics discussed in the book are part of e-learning and distance learning courses conducted by the International School for Scientific Computing.
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Support Vector Machines for Pattern Classification provides a comprehensive resource for the use of SVM?s in pattern classification. The subject area is particularly timely with research on kernel methods increasing rapidly; this book is unique in its focus on classification methods. The characteristic SVM?s are discussed: L1-SVMs and L2-SVMs, lease squares SVMs and linear programming SVMs from both a theoretical and an experimental viewpoint.
SVMs were originally formulated for two-class problems, and an extension to multiclass systems (which are essential for practical use) is not unique. However, in its discussion of several multiclass SVM architectures and the comparison of their performance using real world data, this book provides a unique perspective that researchers and students will find invaluable.
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Understand the fundamentals of the emerging field of fuzzy neural networks, their applications and the most used paradigms with this carefully organized state-of-the-art textbook. Previously tested at a number of noteworthy conference tutorials, the simple numerical examples presented in this book provide excellent tools for progressive learning. UNDERSTANDING NEURAL NETWORKS AND FUZZY LOGIC offers a simple presentation and bottom-up approach that is ideal for working professional engineers, undergraduates, medical/biology majors, and anyone with a nonspecialist background.
Sponsored by:
IEEE Neural Networks Council
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Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration is a handbook for analysts, engineers, and managers involved in developing data mining models in business and government. As you'll discover, fuzzy systems are extraordinarily valuable tools for representing and manipulating all kinds of data, and genetic algorithms and evolutionary programming techniques drawn from biology provide the most effective means for designing and tuning these systems.
You don't need a background in fuzzy modeling or genetic algorithms to benefit, for this book provides it, along with detailed instruction in methods that you can immediately put to work in your own projects. The author provides many diverse examples and also an extended example in which evolutionary strategies are used to create a complex scheduling system.
* Written to provide analysts, engineers, and managers with the background and specific instruction needed to develop and implement more effective data mining systems.
* Helps you to understand the trade-offs implicit in various models and model architectures.
* Provides extensive coverage of fuzzy SQL querying, fuzzy clustering, and fuzzy rule induction.
* Lays out a roadmap for exploring data, selecting model system measures, organizing adaptive feedback loops, selecting a model configuration, implementing a working model, and validating the final model.
* In an extended example, applies evolutionary programming techniques to solve a complicated scheduling problem.
* Presents examples in C, C++, Java, and easy-to-understand pseudo-code.
* Extensive online component, including sample code and a complete data mining workbench.
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Reflecting the tremendous advances that have taken place in the study of fuzzy set theory and fuzzy logic from 1988 to the present, this book not only details the theoretical advances in these areas, but considers a broad variety of applications of fuzzy sets and fuzzy logic as well. Theoretical aspects of fuzzy set theory and fuzzy logic are covered in Part I of the text, including: basic types of fuzzy sets; connections between fuzzy sets and crisp sets; the various aggregation operations of fuzzy sets; fuzzy numbers and arithmetic operations on fuzzy numbers; fuzzy relations and the study of fuzzy relation equations. Part II is devoted to applications of fuzzy set theory and fuzzy logic, including: various methods for constructing membership functions of fuzzy sets; the use of fuzzy logic for approximate reasoning in expert systems; fuzzy systems and controllers; fuzzy databases; fuzzy decision making; and engineering applications. For everyone interested in an introduction to fuzzy set theory and fuzzy logic.
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Written by one of the foremost experts in the field of neural networks, this is the first book to combine the theories and applications or neural networks and fuzzy systems. The book is divided into three sections: Neural Network Theory, Neural Network Applications, and Fuzzy Theory and Applications. It describes how neural networks can be used in applications such as: signal and image processing, function estimation, robotics and control, analog VLSI and optical hardware design; and concludes with a presentation of the new geometric theory of fuzzy sets, systems, and associative memories.
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PLEASE CONTACT MICHELLE FLOMENHOFT AT MCGRAW-HILL FOR A COPY OF THE INSTRUCTOR'S MANUAL at michelle_flomenhoft@mcgraw-hill.com This broad-based textbook will be the first to cover the fundamental theory of fuzzy logic together with simple applications taken from a wide range of engineering disciplines (electrical, mechanical, civil, computer science). Fuzzy logic explores how a computer deals with ambiguity to control innovative "smart" machines, imitating the imprecise thinking of humans. Ross' book offers an introduction to this hot topic for emerging courses at the senior/graduate level. The primary focus of the book is applications in a coherent, organized format, complete with a broad cross-section of applications from such fields as expert systems, control, pattern recognition, and artificial intelligence.
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Computational intelligence is the study of adaptive mechanisms to enable or facilitate intelligent behaviour in complex and changing environments. As such, computational intelligence combines artificial neural networks, evolutionary computing, swarm intelligence and fuzzy systems.This book presents a highly readable and systematic introduction to the fundamentals of computational intelligence. In-depth treatments of the more important and most frequently used techniques are also given. The book provides treatment of computational intelligence in a manner which allows the reader to easily implement the different techniques, and to apply these techniques to solve real-world, complex problems.
Key features include:
- A balanced treatment of the different computational intelligence paradigms
- Inclusion of swarm intelligence
- Coverage of the most recent developments in computational intelligence
- Complete algorithms presented in pseudo-code to allow implementation in any language
- Includes numerous exercises to involve and stimulate the reader further
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Logic for Artificial Intelligence and Information Technology is based on student notes used to teach logic to second year undergraduates and Artificial Intelligence to graduate students at the University of London since1984, first at Imperial College and later at King's College. Logic has been applied to a wide variety of subjects such as theoretical computer science, software engineering, hardware design, logic programming, computational linguistics and artificial intelligence. In this way it has served to stimulate the research for clear conceptual foundations. Over the past 20 years many extensions of classical logic such as temporal, modal, relevance, fuzzy, probabilistic and non-monotoinic logics have been widely used in computer science and artificial intelligence, therefore requiring new formulations of classical logic, which can be modified to yield the effect of the new applied logics. The text introduces classical logic in a goal directed way which can easily deviate into discussing other applied logics. It defines the many types of logics and differences between them. Dov Gabbay, FRSC, FAvH, FRSA, FBCS, is Augustus De Morgan Professor of Logic at the University of London. He has written over 300 papers in logic and over 20 books. He is Editor-in-Chief of several leading journals and has published over 50 handbooks of logic volumes. He is a world authority on applied logics and is one of the directors and founder of the UK charity the International Federation of Computational Logic
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This monograph is a detailed introductory presentation of the key classes of intelligent data analysis methods. The twelve coherently written chapters by leading experts provide complete coverage of the core issues. The first half of the book is devoted to the discussion of classical statistical issues, ranging from the basic concepts of probability, through general notions of inference, to advanced multivariate and time series methods, as well as a detailed discussion of the increasingly important Bayesian approaches and Support Vector Machines. The following chapters then concentrate on the area of machine learning and artificial intelligence and provide introductions into the topics of rule induction methods, neural networks, fuzzy logic, and stochastic search methods. The book concludes with a chapter on Visualization and a higher-level overview of the IDA processes, which illustrates the breadth of application of the presented ideas.
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Computational Intelligence and Feature Selection provides a high level audience with both the background and fundamental ideas behind feature selection with an emphasis on those techniques based on rough and fuzzy sets, including their hybridizations. It introduces set theory, fuzzy set theory, rough set theory, and fuzzy-rough set theory, and illustrates the power and efficacy of the feature selection described through the use of real-world applications and worked examples. Program files implementing major algorithms covered, together with the necessary instructions and datasets, are available on the Web.
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Neural networks and fuzzy systems represent two distinct technologies that deal with uncertainty. This definitive book presents the fundamentals of both technologies, and demonstrates how to combine the unique capabilities of these two technologies for the greatest advantage. Steering clear of unnecessary mathematics, the book highlights a wide range of dynamic possibilities and offers numerous examples to illuminate key concepts. It also explores the value of relating genetic algorithms and expert systems to fuzzy and neural technologies.
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