Book List about AI

BOOKS OF INTEREST 

Introduction to Algorithms 

BOOK WEB PAGE: http://mitpress.mit.edu/algorithms/

There are books on algorithms that are rigorous but incomplete and others that cover masses of material but lack rigor. Introduction to Algorithms combines rigor and comprehensiveness. The book covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.

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Artificial Intelligence : A Modern Approach 

BOOK WEB PAGE: http://aima.cs.berkeley.edu/

Artificial Intelligence (AI) is a big field, and this is a big book. We have tried to explore the full breadth of the field, which encompasses logic, probability, and continuous mathematics; perception, reasoning, learning, and action; and everything from microelectronic devices to robotic planetary explorers. The book is also big because we go into some depth in presenting results, although we strive to cover only the most central ideas in the main part of each chapter. Pointers are given to further results in the bibliographical notes at the end of each chapter.

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How to Solve It: A New Aspect of Mathematical Method 

BOOK WEB PAGE: http://www.amazon.com/How-Solve-Mathematical-Princeton-Science/dp/069111966X 

In this best-selling classic, George Polya revealed how the mathematical method of demonstrating a proof or finding an unknown can be of help in attacking any problem that can be "reasoned" out--from building a bridge to winning a game of anagrams. Generations of readers have relished Polya's deft instructions on stripping away irrelevancies and going straight to the heart of a problem. How to Solve It popularized heuristics, the art and science of discovery and invention. It has been in print continuously since 1945 and has been translated into twenty-three different languages.

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Reactive Search and Intelligent Optimization 

BOOK WEB PAGE: http://www.reactive-search.org/thebook/

Reactive Search and Intelligent Optimization is an excellent introduction to the main principles of reactive search, as well as an attempt to develop some fresh intuition for the approaches. The book looks at different optimization possibilities with an emphasis on opportunities for learning and self-tuning strategies. While focusing more on methods than on problems, problems are introduced wherever they help make the discussion more concrete, or when a specific problem has been widely studied by reactive search and intelligent optimization heuristics.

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Autonomous Search 

BOOK WEB PAGE: http://www.springer.com/computer/ai/book/978-3-642-21433-2

Autonomous search (AS) represents a new research field. Its major strength and originality consist in the fact that problem solvers can now perform self-improvement operations based on analysis of the performances of the solving process -- including short-term reactive reconfiguration and long-term improvement through self-analysis of the performance, offline tuning and online control, and adaptive control and supervised control. Autonomous search "crosses the chasm" and provides engineers and practitioners with systems that are able to autonomously self-tune their performance while effectively solving problems. This is the first book dedicated to this topic, and it can be used as a reference for researchers, engineers, and postgraduates in the areas of constraint programming, machine learning, evolutionary computing, and feedback control theory. After the editors' introduction to autonomous search, the chapters are focused on tuning algorithm parameters, autonomous complete (tree-based) constraint solvers, autonomous control in metaheuristics and heuristics, and future autonomous solving paradigms.

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Stochastic Local Search : Foundations & Applications 

BOOK WEB PAGE: http://www.sls-book.net/

The book establishes a landmark in the broad field of this type of algorithms that are also known as metaheuristics. This book is the first in unifying the dispersed field of Stochastic Local Search (SLS) algorithms. Written in a clear and easy-to-read style, the book tries to cover all possible audiences, from graduate students or doctoral students to practitioners and researchers. The book more than accomplishes its main goal of covering both the foundations and the many applications of SLS algorithms. It provides an excellent empirical scientific methodology geared towards the successful application of SLS algorithms in practice. Furthermore, it presents advances in the explanation of complex SLS behavior, something which is much needed and refreshingly new in the field.

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Search Methodologies 

BOOK WEB PAGE: http://www.amazon.com/Search-Methodologies-Introductory-Optimization-Techniques/dp/0387234608

Search Methodologies is a tutorial survey of the methodologies that are at the confluence of several fields: Computer Science, Mathematics and Operations Research. It is a carefully structured and integrated treatment of the major technologies in optimization and search methodology. The book is made up of 18 chapters. The chapter authors are drawn from across Computer Science and Operations Research and include some of the world's leading authorities in their field. Topical chapters in the book are highlighted in the contents. The result is a major state-of-the-art tutorial text of the main optimization and search methodologies available to researchers, students and practitioners across discipline domains in applied science. It can be used as a textbook or a reference book to learn and apply these methodologies to a wide range of today's problems. It has been written by some of the world's most well known authors in the field.

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Metaheuristics: From Design to Implementation 

BOOK WEB PAGE: http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470278587.html

This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a toolbox for: 1) Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems, 2) Designing efficient metaheuristics for multi-objective optimization problems, 3) Designing hybrid, parallel, and distributed metaheuristics, 4) Implementing metaheuristics on sequential and parallel machines.

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Handbook of Metaheuristics 

BOOK WEB PAGE: http://www.springer.com/business+%26+management/operations+research/book/978-1-4419-1663-1

The first edition of the Handbook of Metaheuristics was published in 2003 under the editorship of Fred Glover and Gary A. Kochenberger. Given the numerous developments observed in the field of metaheuristics in recent years, it appeared that the time was ripe for a second edition of the Handbook. When Glover and Kochenberger were unable to prepare this second edition, they suggested that Michel Gendreau and Jean-Yves Potvin should take over the editorship, and so this important new edition is now available. Through its 21 chapters, this second edition is designed to provide a broad coverage of the concepts, implementations and applications in this important field of optimization. Original contributors either revised or updated their work, or provided entirely new chapters. The Handbook now includes updated chapters on the best known metaheuristics, including simulated annealing, tabu search, variable neighborhood search, scatter search and path relinking, genetic algorithms, memetic algorithms, genetic programming, ant colony optimization, multi-start methods, greedy randomized adaptive search procedure, guided local search, hyper-heuristics and parallel metaheuristics. It also contains three new chapters on large neighborhood search, artificial immune systems and hybrid metaheuristics. The last four chapters are devoted to more general issues related to the field of metaheuristics, namely reactive search, stochastic search, fitness landscape analysis and performance comparison.

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Hybrid Metaheuristics 

BOOK WEB PAGE: http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-540-78294-0

Optimization problems are of great importance in many fields. They can be tackled, for example, by approximate algorithms such as metaheuristics. Examples of metaheuristics are simulated annealing, tabu search, evolutionary computation, iterated local search, variable neighborhood search, and ant colony optimization. In recent years it has become evident that a skilled combination of a metaheuristic with other optimization techniques, a so called hybrid metaheuristic, can provide a more efficient behavior and a higher flexibility. This is because hybrid metaheuristics combine their advantages with the complementary strengths of, for example, more classical optimization techniques such as branch and bound or dynamic programming.

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How to Solve It: Modern Heuristics 

BOOK WEB PAGE: http://www.amazon.com/How-Solve-Heuristics-Zbigniew-Michalewicz/dp/3540660615

This book is the only source that provides comprehensive, current, and correct information on problem solving using modern heuristics. It covers classic methods of optimization, including dynamic programming, the simplex method, and gradient techniques, as well as recent innovations such as simulated annealing, tabu search, and evolutionary computation. Integrated into the discourse is a series of problems and puzzles to challenge the reader. The book is written in a lively, engaging style and is intended for students and practitioners alike. Anyone who reads and understands the material in the book will be armed with the most powerful problem solving tools currently known.

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Metaheuristics for Hard Optimization: Methods and Case Studies 

BOOK WEB PAGE: http://www.amazon.com/Metaheuristics-Hard-Optimization-Methods-Studies/dp/354023022X

Everyday, the engineers and the decision makers are confronted with problems of growing complexity, which emerge in diverse technical sectors, e.g. in operations research, the design of mechanical systems, image processing, and particularly in electronics (C.A.D. of electrical circuits, placement and routing of components, improvement of the performances or the manufacture yield of circuits, characterization of equivalent schemas, training of fuzzy rule bases or neural networks . . . ). The problem to be solved can be often expressed as an optimization problem.

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Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies 

BOOK WEB PAGE: http://baibook.epfl.ch/ 

New approaches to artificial intelligence spring from the idea that intelligence emerges as much from cells, bodies, and societies as it does from evolution, development, and learning. Traditionally, artificial intelligence has been concerned with reproducing the abilities of human brains; newer approaches take inspiration from a wider range of biological structures that that are capable of autonomous self-organization. Examples of these new approaches include evolutionary computation and evolutionary electronics, artificial neural networks, immune systems, biorobotics, and swarm intelligence—to mention only a few. This book offers a comprehensive introduction to the emerging field of biologically inspired artificial intelligence that can be used as an upper-level text or as a reference for researchers.

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Combinatorial Optimization: Theory and Algorithms 

BOOK WEB PAGE: http://www.amazon.com/Combinatorial-Optimization-Theory-Algorithms-Combinatorics/dp/3540431543 

This comprehensive textbook on combinatorial optimization puts special emphasis on theoretical results and algorithms with provably good performance, in contrast to heuristics. It has arisen as the basis of several courses on combinatorial optimization and more special topics at graduate level. Since the complete book contains enough material for at least four semesters (4 hours a week), one usually selects material in a suitable way. The book contains complete (but concise) proofs, also for many deep results, some of which did not appear in a book before. Many very recent topics are covered as well, and many references are provided. Thus this book represents the state-of-the-art of combinatorial optimization.

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Recommender Systems: An Introduction 

BOOK WEB PAGE: http://www.amazon.com/Recommender-Systems-Introduction-Dietmar-Jannach/dp/0521493366 

In this age of information overload, people use a variety of strategies to make choices about what to buy, how to spend their leisure time, and even whom to date. Recommender systems automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. This book offers an overview of approaches to developing state-of-the-art recommender systems. The authors present current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. They also discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical case studies. The final chapters cover emerging topics such as recommender systems in the social web and consumer buying behavior theory. Suitable for computer science researchers and students interested in getting an overview of the field, this book will also be useful for professionals looking for the right technology to build real-world recommender systems.

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Automated Planning: Theory & Practice 

BOOK WEB PAGE: http://www.amazon.com/Automated-Planning-Practice-Artificial-Intelligence/dp/1558608567 

One motivation for studying automated planning is very practical: the need for information processing tools that provide affordable and efficient planning resources. Such tools are needed for use in complex and changing tasks that involve demanding safety and/or efficiency requirements. They are needed for integrating more autonomy and intelligent control capabilities in complex artifacts. Automated planning technology is already capable enough to be useful in a variety of demanding applications, in areas ranging from controlling space vehicles and robots to deploying emergency operations and playing the game of bridge.

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Handbook of Scheduling: Algorithms, Models, and Performance Analysis 

BOOK WEB PAGE: http://www.amazon.com/Handbook-Scheduling-Algorithms-Performance-Analysis/dp/1584883979 

Over the past few years, the field of scheduling has changed dramatically because of the complexity of the problems now involved. This has led many researchers to develop approximation algorithms that can handle these more difficult kinds of scheduling problems. This in turn has resulted in enormous growth of the field and a wide body of knowledge that has never been pulled together into one volume-until now. Handbook of Scheduling: Algorithms, Models, and Performance Analysis collects all of the up-to-date information on approximation algorithms into one resource that will prove useful to a wide range of users from computer science, industrial engineering, operations research, and management science.

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An Introduction to Genetic Algorithms 

BOOK WEB PAGE: http://mitpress.mit.edu/catalog/item/default.asp?tid=5974&ttype=2 

Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics-particularly in machine learning, scientific modeling, and artificial life-and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics.

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Introduction to Evolutionary Computing 

BOOK WEB PAGE: http://www.cs.vu.nl/~gusz/ecbook/ecbook.html

Evolutionary Computing is the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. These techniques are being increasingly widely applied to a variety of problems, ranging from practical applications in industry and commerce to leading-edge scientific research. This book presents the first complete overview of this exciting field aimed directly at lecturers and graduate and undergraduate students. This book is also meant for those who wish to apply evolutionary computing to a particular problem or within a given application area. To this group the book is valuable because it presents EC as something to be used rather than just studied. Last, but not least, this book contains quick-reference information on the current state-of-the-art in a wide range of related topics, so it is of interest not just to evolutionary computing specialists but to researchers working in other fields.

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Bioinspired Computation in Combinatorial Optimization - Algorithms and Their Computational Complexity 

BOOK WEB PAGE:http://www.bioinspiredcomputation.com/

Bioinspired computation methods such as evolutionary algorithms and ant colony optimization are being applied successfully to complex engineering problems and to problems from combinatorial optimization, and with this comes the requirement to more fully understand the computational complexity of these search heuristics. This is the first textbook covering the most important results achieved in this area. The authors study the computational complexity of bioinspired computation and show how runtime behavior can be analyzed in a rigorous way using some of the best-known combinatorial optimization problems -- minimum spanning trees, shortest paths, maximum matching, covering and scheduling problems. A feature of the book is the separate treatment of single- and multiobjective problems, the latter a domain where the development of the underlying theory seems to be lagging practical successes. This book will be very valuable for teaching courses on bioinspired computation and combinatorial optimization. Researchers will also benefit as the presentation of the theory covers the most important developments in the field over the last 10 years. Finally, with a focus on well-studied combinatorial optimization problems rather than toy problems, the book will also be very valuable for practitioners in this field.

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Evolutionary Computation: Theory and Applications 

BOOK WEB PAGE: http://www.amazon.co.uk/Evolutionary-Computation-Applications-X-Yao/dp/9810223064

Evolutionary computation is the study of computational systems which use ideas and get inspiration from natural evolution and adaptation. This book is devoted to the theory and application of evolutionary computation. It is a self-contained volume which covers both introductory material and selected advanced topics. The book can roughly be divided into two major parts: the introductory one and the one on selected advanced topics. Each part consists of several chapters which present an in-depth discussion of selected topics. A strong connection is established between evolutionary algorithms and traditional search algorithms. This connection enables us to incorporate ideas in more established fields into evolutionary algorithms. The book is aimed at a wide range of readers. It does not require previous exposure to the field since introductory material is included. It will be of interest to anyone who is interested in adaptive optimization and learning. People in computer science, artificial intelligence, operations research, and various engineering fields will find it particularly interesting.

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The Nature of Computation 

BOOK WEB PAGE: http://www.nature-of-computation.org

Computational complexity is one of the most beautiful fields of modern mathematics, and it is increasingly relevant to other sciences ranging from physics to biology. This book gives a lucid and playful explanation of the field, starting with P and NP-completeness. The authors explain why the P vs. NP problem is so fundamental, and why it is so hard to resolve. They then lead the reader through the complexity of mazes and games; optimization in theory and practice; randomized algorithms, interactive proofs, and pseudorandomness; Markov chains and phase transitions; and the outer reaches of quantum computing. At every turn, they use a minimum of formalism, providing explanations that are both deep and accessible. The book is intended for graduates and undergraduates, scientists from other areas who have long wanted to understand this subject, and experts who want to fall in love with this field all over again.

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Theory of Randomized Search Heuristics: Foundations and Recent Developments 

BOOK WEB PAGE: http://www.worldscibooks.com/compsci/7438.html

Randomized search heuristics such as evolutionary algorithms, genetic algorithms, evolution strategies, ant colony and particle swarm optimization turn out to be highly successful for optimization in practice. The theory of randomized search heuristics, which has been growing rapidly in the last five years, also attempts to explain the success of the methods in practical applications. This book covers both classical results and the most recent theoretical developments in the field of randomized search heuristics such as runtime analysis, drift analysis and convergence. Each chapter provides an overview of a particular domain and gives insights into the proofs and proof techniques of more specialized areas. Open problems still remain widely in randomized search heuristics -- being a relatively young and vast field. These problems and directions for future research are addressed and discussed in this book. The book will be an essential source of reference for experts in the domain of randomized search heuristics and also for researchers who are involved or ready to embark in this field. As an advanced textbook, graduate students will benefit from the comprehensive coverage of topics.

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Experimental Methods for the Analysis of Optimization Algorithms 

BOOK WEB PAGE: http://www.springer.com/computer/theoretical+computer+science/book/978-3-642-02537-2

In operations research and computer science it is common practice to evaluate the performance of optimization algorithms on the basis of computational results, and the experimental approach should follow accepted principles that guarantee the reliability and reproducibility of results. However, computational experiments differ from those in other sciences, and the last decade has seen considerable methodological research devoted to understanding the particular features of such experiments and assessing the related statistical methods. This book consists of methodological contributions on different scenarios of experimental analysis. The first part overviews the main issues in the experimental analysis of algorithms, and discusses the experimental cycle of algorithm development; the second part treats the characterization by means of statistical distributions of algorithm performance in terms of solution quality, runtime and other measures; and the third part collects advanced methods from experimental design for configuring algorithms on a specific class of instances with the goal of using the least amount of experimentation. The contributors include leading scientists in algorithm design, statistical design, optimization and heuristics, and every chapter is enriched with case studies.This book is written for researchers and practitioners of operations research and computer science who wish to improve the experimental assessment of their optimization algorithms and thus improve their design.

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Handbook of Memetic Algorithms 

BOOK WEB PAGE: http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-23246-6

Memetic Algorithms (MAs) are computational intelligence structures combining multiple and various operators in order to address optimization problems. The combination and interaction amongst operators evolves and promotes the diffusion of the most successful units and generates an algorithmic behavior which can handle complex objective functions and hard fitness landscapes. “Handbook of Memetic Algorithms” organizes, in a structured way, all the the most important results in the field of MAs since their earliest definition until now. A broad review including various algorithmic solutions as well as successful applications is included in this book. Each class of optimization problems, such as constrained optimization, multi-objective optimization, continuous vs combinatorial problems, uncertainties, are analysed separately and, for each problem, memetic recipes for tackling the difficulties are given with some successful examples. Although this book contains chapters written by multiple authors, a great attention has been given by the editors to make it a compact and smooth work which covers all the main areas of computational intelligence optimization. It is not only a necessary read for researchers working in the research area, but also a useful handbook for practitioners and engineers who need to address real-world optimization problems. In addition, the book structure makes it an interesting work also for graduate students and researchers is related fields of mathematics and computer science.

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Evolutionary Computation in Bioinformatics 

BOOK WEB PAGE: http://www.elsevier.com/wps/find/bookdescription.cws_home/680642/description

Bioinformatics has never been as popular as it is today. The genomics revolution is generating so much data in such rapid succession that it has become difficult for biologists to decipher. In particular, there are many problems in biology that are too large to solve with standard methods. Researchers in evolutionary computation (EC) have turned their attention to these problems. They understand the power of EC to rapidly search very large and complex spaces and return reasonable solutions. While these researchers are increasingly interested in problems from the biological sciences, EC and its problem-solving capabilities are generally not yet understood or applied in the biology community. This book offers a definitive resource to bridge the computer science and biology communities. Gary Fogel and David Corne, well-known representatives of these fields, introduce biology and bioinformatics to computer scientists, and evolutionary computation to biologists and computer scientists unfamiliar with these techniques. The fourteen chapters that follow are written by leading computer scientists and biologists who examine successful applications of evolutionary computation to various problems in the biological sciences.

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Introduction to Machine Learning 

BOOK WEB PAGE: http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=10341

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. It discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The book can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.

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Pattern Recognition and Machine Learning 

BOOK WEB PAGE: http://research.microsoft.com/en-us/um/people/cmbishop/prml/

This leading textbook provides a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. This is the first machine learning textbook to include a comprehensive coverage of recent developments such as probabilistic graphical models and deterministic inference methods, and to emphasize a modern Bayesian perspective. It is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. This hard cover book has 738 pages in full colour, and there are 431 graded exercises (with solutions available below).

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The Elements of Statistical Machine Learning 

BOOK WEB PAGE: http://www-stat.stanford.edu/~tibs/ElemStatLearn/

During the past decade has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book descibes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting--the first comprehensive treatment of this topic in any book.

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Convex Optimization 

BOOK WEB PAGE: http://www.stanford.edu/~boyd/cvxbook/

Convex optimization problems arise frequently in many different fields. A comprehensive introduction to the subject, this book shows in detail how such problems can be solved numerically with great efficiency. The focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. The text contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance, and economics. Convex Optimization book is a very readable and inspiring introduction to this modern field of research.

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Neuro-Dynamic Programming (Optimization and Neural Computation Series) 

BOOK WEB PAGE: http://www.amazon.com/Neuro-Dynamic-Programming-Optimization-Neural-Computation/dp/1886529108

This is the first textbook that fully explains the neuro-dynamic programming/reinforcement learning methodology, which is a recent breakthrough in the practical application of neural networks and dynamic programming to complex problems of planning, optimal decision making, and intelligent control. Neuro-dynamic programming uses neural network approximations to overcome the "curse of dimensionality" and the "curse of modeling" that have been the bottlenecks to the practical application of dynamic programming and stochastic control to complex problems. The methodology allows systems to learn about their behavior through simulation, and to improve their performance through iterative reinforcement. This book provides the first systematic presentation of the science and the art behind this exciting and far-reaching methodology. The book develops a comprehensive analysis of neuro-dynamic programming algorithms, and guides the reader to their successful application through case studies from complex problem areas.

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Reinforcement Learning: An Introduction 

BOOK WEB PAGE: http://www.cs.ualberta.ca/~sutton/book/the-book.html

Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

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An Introduction to Game Theory 

BOOK WEB PAGE: http://www.economics.utoronto.ca/osborne/igt/index.html

Game-theoretic reasoning pervades economic theory and is used widely in other social and behavioral sciences. An Introduction to Game Theory, by Martin J. Osborne, presents the main principles of game theory and shows how they can be used to understand economic, social, political, and biological phenomena. The book introduces in an accessible manner the main ideas behind the theory rather than their mathematical expression. All concepts are defined precisely, and logical reasoning is used throughout. The book requires an understanding of basic mathematics but assumes no specific knowledge of economics, political science, or other social or behavioral sciences. Coverage includes the fundamental concepts of strategic games, extensive games with perfect information, and coalitional games; the more advanced subjects of Bayesian games and extensive games with imperfect information; and the topics of repeated games, bargaining theory, evolutionary equilibrium, rationalizability, and maxminimization. The book offers a wide variety of illustrations from the social and behavioral sciences and more than 280 exercises. Each topic features examples that highlight theoretical points and illustrations that demonstrate how the theory may be used. Explaining the key concepts of game theory as simply as possible while maintaining complete precision, An Introduction to Game Theory is ideal for undergraduate and introductory graduate courses in game theory.

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Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence 

BOOK WEB PAGE: http://www.amazon.com/Minds-Computers-Introduction-Philosophy-Intelligence/dp/0748620990

Could a computer have a mind? What kind of machine would this be? Exactly what do we mean by "mind" anyway? The notion of the "intelligent'"machine, while continuing to feature in numerous entertaining and frightening fictions, has also been the focus of a serious and dedicated research tradition. Reflecting on these fictions, and on the research tradition that pursues "Artificial Intelligence", raises a number of vexing philosophical issues. Minds and Computers offers an engaging, coherent, and highly approachable interdisciplinary introduction to the Philosophy of Artificial Intelligence. Readers are presented with introductory material from each of the disciplines which constitute Cognitive Science: Philosophy, Neuroscience, Psychology, Computer Science, and Linguistics. Throughout, readers are encouraged to consider the implications of this disparate and wide-ranging material for the possibility of developing machines with minds. And they can expect to develop a foundation for philosophically responsible engagement with A.I., a sound understanding of Philosophy of Mind and of computational theory, and a good feel for cross-disciplinary analysis.

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Ant Colony Optimization 

BOOK WEB PAGE: http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=10139

The book first describes the translation of observed ant behavior into working optimization algorithms. The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. AntNet, an ACO algorithm designed for the network routing problem, is described in detail. The authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms.

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Handbook of Constraint Programming (Foundations of Artificial Intelligence) 

BOOK WEB PAGE: http://www.elsevier.com/wps/find/bookdescription.cws_home/708863/description

Constraint programming is a powerful paradigm for solving combinatorial search problems that draws on a wide range of techniques from artificial intelligence, computer science, databases, programming languages, and operations research. Constraint programming is currently applied with success to many domains, such as scheduling, planning, vehicle routing, configuration, networks, and bioinformatics. The aim of this handbook is to capture the full breadth and depth of the constraint programming field and to be encyclopedic in its scope and coverage. While there are several excellent books on constraint programming, such books necessarily focus on the main notions and techniques and cannot cover also extensions, applications, and languages. The handbook gives a reasonably complete coverage of all these lines of work, based on constraint programming, so that a reader can have a rather precise idea of the whole field and its potential. Of course each line of work is dealt with in a survey-like style, where some details may be neglected in favor of coverage. However, the extensive bibliography of each chapter will help the interested readers to find suitable sources for the missing details. Each chapter of the handbook is intended to be a self-contained survey of a topic, and is written by one or more authors who are leading researchers in the area.

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Handbook of Satisfiability 

BOOK WEB PAGE: http://www.iospress.nl/book/handbook-of-satisfiability/

The topics of the handbook span practical and theoretical research on SAT and its applications and include search algorithms, heuristics, analysis of algorithms, hard instances, randomized formulae, problem encodings, industrial applications, solvers, simplifiers, tools, case studies and empirical results. SAT is interpreted in a rather broad sense. Besides propositional satisfiability it includes the domain of quantified Boolean formulae (QBF), constraints programming techniques (CSP) for word-level problems and their propositional encoding and particularly satisfiability modulo theories (SMT). The handbook aims to capture the full breadth and depth of SAT and to bundle significant progress and advances in automated solving. It covers the main notions and techniques and introduces various formal extensions. Each area is dealt with in a survey-like style, where some details may be neglected in favor of coverage. The extensive bibliography concluding each chapter will help the interested reader to find his way to master necessary details. The intended audience of the handbook consists of researchers, graduate students, upper-year undergraduates, and practitioners who wish to learn about the state of the art in actual solving. Limited prior knowledge about the field is assumed. The handbook also aims to assist researchers from other fields in applying ideas and methods to their own work.

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Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines 

BOOK WEB PAGE: http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=10196

Evolutionary robotics is a new technique for the automatic creation of autonomous robots. Inspired by the Darwinian principle of selective reproduction of the fittest, it views robots as autonomous artificial organisms that develop their own skills in close interaction with the environment and without human intervention. Drawing heavily on biology and ethology, it uses the tools of neural networks, genetic algorithms, dynamic systems, and biomorphic engineering. The resulting robots share with simple biological systems the characteristics of robustness, simplicity, small size, flexibility, and modularity. This book describes the basic concepts and methodologies of evolutionary robotics and the results achieved so far. An important feature is the clear presentation of a set of empirical experiments of increasing complexity. Software with a graphic interface, freely available on a Web page, will allow the reader to replicate and vary (in simulation and on real robots) most of the experiments.

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Evolutionary Robotics: From Algorithms to Implementations 

BOOK WEB PAGE: http://www.amazon.com/Evolutionary-Robotics-Algorithms-Implementations-Intelligent/dp/9812568700

This invaluable book comprehensively describes evolutionary robotics and computational intelligence, and how different computational intelligence techniques are applied to robotic system design. It embraces the most widely used evolutionary approaches with their merits and drawbacks, presents some related experiments for robotic behavior evolution and the results achieved, and shows promising future research directions. Clarity of explanation is emphasized such that a modest knowledge of basic evolutionary computation, digital circuits and engineering design will suffice for a thorough understanding of the material. The book is ideally suited to computer scientists, practitioners and researchers keen on computational intelligence techniques, especially the evolutionary algorithms in autonomous robotics at both the hardware and software levels.

http://allserv.kahosl.be/~mustafa.misir/books_r.html