Artificial Intelligence Books

I have been interested in AI for a very long time. After reading this book I was enthralled to try some collective learning programming of my own. The book gave a very solid description of the algorithms involved, and was enough to allow me to build my own simple collective learning algorithms. Anyone intereted in learning or neural networks should buy this book.

This is a must read for anyone serious about Machine Learning and Cognition! Peter Bock, an internationally recognized scientist, presents his theories and associated technology for the coming generations of adaptive intelligent machines. He discusses the processes of cognition, postulates a fundamental adaptive building block for assembling very large-scale collective learning systems, and proposes a design for a machine that could exhibit the full range of cognitive capabilities of the human mind. The book includes discussion of game theory, artificial cognition, and the philosophical issues raised by the prospect of creating machines that exhibit human-like cognition. The book is an easy and entertaining read. I highly recomend it.

I think any person who major in target tracking system related to the Kalman filter must see this book. This book present the fundamentals of state estimation theory and the tools for the design of state-of-the-art algorithms for target tracking.

The book covers the basic concepts and estimation techniques for static and dynamic systems, linear and nonlinear, as well as adaptive estiomation. This constitutes a one semester graduate course in estimation theory in an electrical/systems engineering program.

The discussion deals mainly with discrete time estimation algorithms, which are natural for digital computer implementation. The basic state estimation algorithm-the Kalman filter-is presented in discrete as well as in continuous time. The use of the estimation algorithms is illustrated on kinematic motion models because they reveal all the major issues and in particular the subtleties encountered in estimation, and this serves as an introdution to tracking.

Guidelines for tracking filter design-selection of the filter design parameters-are given and illustrated in several examples.

At the end of each chapter, a number of problems that enhance the understanding of the theory and the connection of the theoretical material to the real world are given.

And I have this book as text for my paper.

I don't usually write online reviews but this book is so clear and useful that I really want to recommend it to others. It is well written with a good outline and summary for every chapter. It also has a pretty diverse range of topics on estimation, including an introductory chapter on basic estimation approaches (e.g., ML, MAP, least squares), and very practical extensions (e.g., state augmentation, square-root filters). Even though I am not in EE and some of the examples are thus not particularly helpful to me, I still find this book one of the best of all the estimation/Kalman filter books out there.

This is a not too technical book, introducing you to evolutionary robotics. It covers a lot of interesting work done in the field over the last few years.

Using genetic algorithms it is possible to construct the brain of a robot ( Neural network ), and even (though harder) the hardware itself. The book gives an introduction to genetic algortihms & artificial neural networks, but the reader should still be somewhat familiar with these concepts before buying this book.

After reading this book, I did one of the experiments mentioned in the book. While I only did this with a freeware Kephara robot simulator, it was still fun.

This is a not too technical book, introducing you to evolutionary robotics. It covers a lot of interesting work done in the field over the last few years.

Using genetic algorithms it is possible to construct the brain of a robot ( Neural network ), and even (though harder) the hardware itself. The book gives an introduction to genetic algortihms & artificial neural networks, but the reader should still be somewhat familiar with these concepts before buying this book.

After reading this book, I did one of the experiments mentioned in the book. While I only did this with a freeware Kephara robot simulator, it was still fun.

I was a former teaching assistant this past summer for a lower division computer science course at UC Berkeley. We used this book as our primary text. From several semesters of teaching introductory computer science courses I can say that this book has proven to be an indispensible item for me. Ranging from concise writing to thought provoking questions, it is an excellent introduction to future computer scientists. I recommend this to anyone.

The first part of this book will give the basic programming `how to' knowledge: Common abstractions and basic program design.

The second part introduces Computer Science and will show you what others have been doing with the techniques explained in the first part. It will give a panoramic view of modern CS: databases, operating systems, artificial intelligence, compilers, `soft-computing', etc...

Only the chapter on compilers seems a bit confusing, everything else is clearly explained.

You won't need a good background in maths to follow it.

This book is an excellent introduction to logic as it is applied to logic programming. It begins with a set of mathematical preliminaries, mathematical induction, trees, multisets, ordinals and cardinals. This is a ten-page refresher that most readers will be able to skip. Chapter two covers propositional logic, but this is not a standard overview of the subject. Advanced theorems such as the Soundness and Completeness theorems are stated and succinctly proven.
First-order logic is the topic of chapter three, and the coverage is thorough with it being aimed directly at the topic of chapter four, which is program-definability. In any complex programming project, one of the most difficult problems to resolve is how the data is to be represented in the computer. Chapter four explains how domains such as the natural numbers, binary notation and lists can be represented using the Herbrand Universe model.
The final three chapters cover linear resolution, infinite derivations, computability and negation. Mathematical proofs can be based on either verification of a positive or affirmation of a negative. Linear resolution can only deduce positive information from programs, infinite derivations and negation can be used to affirm a negative, indirectly deducing the opposite positive. Exercises are included at the end of each chapter, but no solutions are given.
This book is the best place to begin if you want to understand the mathematical foundations of logic programming. However, at least one course in mathematical logic is a precondition to that understanding.

This is a book that presents logic programming paradigm in very modern, clear and concise way.

The book does not contain description of any particular logic programming language, but gives mathematical foundations of logic programming, such as, Herbrand interpretations, unification, Resolution, SLDNF-resolution, the least Herbrand models and fix-points, computability...

Worth to buy.

This book shows using the program how fuzzy logic works.
There is a lot in the book
and it needs to be read a few times.

Read also

FUZZY LOGIC: THE REVOLUTIONARY COMPUTER TECHNOLOGY THAT IS CHANGING OUR WORLD
by Daniel Mcneil

The ideas in both are amazing.
page 107 is great here.

It's not theoretical or programming based, rather a superb description of what fuzzy logic can DO. The software is getting a bit out of date, but you can always download the latest demo version off their web site.

The book gives a clear and intuitive description of fuzzy math and its application. The discussions are done neatly and nicely. It doesn't get into hardcore math discussions very much; instead, it focuses on applications and practice. Therefore, it doesn't take much time to get the idea, and you are not bothered with very complex math proofs. The level of the theory is also limited to what needed in application. To my belief this is one of the best books for engineers and applied mathematicians.

This book is a kind of essential book. The growth in Fuzzy Science has showed more and more possibilities in applications of its concepts. The book shows the vast applicability of Fuzzy in details and how this new approach can be useful in domains with high degree of uncertainty.

I really can recommend the reading of this book for all persons who wishes understand more about Fuzzy !

Well organized, funny introduction, and good examples from real commercial computer games. Uses usage statistics to prioritize tests.

Traditional program testing has come a long way in 50 years but the complexity, time constraints and variety of situations facing game testers is at the edge of the software testing world.

The authors combine authentic experience with Computer Games and Game Testing in a logical, pragmatic and interesting way. They long ago reasoned that it is very costly (if not sometimes impossible) to test all possible paths through a game's options and through the application of some insightful statistical approaches present ways to reduce the total number of tests required under certain assumptions. To me this one insight was worth the cost of the book.

Beyond this outstanding statistical testing insight for the workers in the field, the expository material that covers Games is exceptionally good for the individual trying to put the whole field in perspective. In Part I they address "Being a Game Tester" (with a side venture later in the book to identify different game testing personalities) followed by a good discussion of "Why Testing is Important". Part II talks about the "Making of a Game" while Part III introduces the concepts and vocabulary of Testing. In Part IV the authors bring in the concept of Combinatorial Testing ( another statistical approach) and present it in a step by step way so that the tester can perform the testing suite without knowing all of the theory behind that approach. "Test Flow Diagrams", "Cleanroom Testing" and "Test Trees" adds very modern disciplined techniques to the testing tool process that will prove of value. Part V discusses some advanced testing ideas all of which are very practical in the fast evolving world of Games.

The authors seem to have been brought together and inspired to write just at the time when the field realizes that loosely structured ad-hoc testing may be costing more than the field can afford.

It is a very nice book for a beginner. It is a well-organized and excellent text covering fully up-to-date treatment of genetic algorithms in industrial engineering and operationas research. Especially, its unique, creative and intuitive approach makes the complex subject transparent and understandable. I fond the book very much and used the materials in my classes.

This is a very nice book for beginners. It is a well-organized and excellent text covering fully up-to-date treatment of genetic algorithms in industrial engineering and operationas research. Especially, its unique, creative and intuitive approach makes the complex subject transparent and understandable. I fond the book very much and used the materials in my classes.

The volume brings an outstanding presentation of the major issues, ideas, concepts and algorithms to design and develop fuzzy systems using gentic algorithms. A field of major relevance for researchers and practioners, genetic fuzzy systems provides a major methodological substract of significant impact in practice. The book is unique in its contents and presentation. Chapters begin with the key concepts and smoothly grows to advanced concepts in a clear and very understandable and motivating way. The material mirrors the state of the art in the area of genetic fuzzy systems and contains the most recent results available until its publication. Written by renowned, internationally recognized researchers, the book is mandatory to all who are interested in the field of computational intelligence, its foundations and applications.

In recent years, a great number of publications have explored the use of genetic algorithms as a tool for designing fuzzy
systems. Genetic Fuzzy Systems explores and discusses this symbiosis of evolutionary computation and fuzzy logic. The book summarizes and analyzes the novel field of genetic fuzzy systems, paying special attention to genetic algorithms that adapt and learn
the knowledge base of a fuzzy-rule-based system. It introduces the general concepts, foundations and design principles of genetic fuzzy
systems and covers the topic of genetic tuning of fuzzy systems. It also introduces the three fundamental approaches to genetic learning
processes in fuzzy systems: the Michigan, Pittsburgh and Iterative-learning methods. Finally, it explores hybrid genetic fuzzy systems such as
genetic fuzzy clustering or genetic neuro-fuzzy systems and describes a number of applications from different areas. Genetic Fuzzy System represents a comprehensive treatise on the design of the fuzzy-rule-based systems using genetic algorithms, both from
a theoretical and a practical perspective. It is a valuable compendium for scientists and engineers concerned with research and applications in
the domain of fuzzy systems and genetic algorithms.