Artificial Intelligence Books

This book takes the reader on an overview of the history of life (after a review of the formation of earth), culminating in the human mind and then onto speculations about the future of the mind (robotic minds to be sent into the cosmos as immortal explorers). I read this because I was curious about the proto-mammal(s) from which all sprung after the passing of the age of the dinosaurs.

I was immediately linked into a graceful narrative chock full of ideas, from the development of an acute sense of smell - enlarging certain portions of the mammal brain beyond that of its reptilian competitors - for (warm-blooded) night stealth, to the evolution of the human thumb and, finally, the passage from tree to veldt of proto-humans (where they needed the brains to make tools). It is a breathtaking adventure that is also a quick read with ideas that stick. There are also beautiful illustrations that lighten the text and help to engrave it in memory.

Interestingly, I found this book because of a critique by SJ Gould, in which Gould argued that Jastrow's interpretation made the human ascent look inevitable as well as placed man at the top of the animal hierarchy as inherently superior because of the human brain. Not so, argued Gould, who said that every species is the result of their own evolutionary pressures and hence were superior survivors in their original environmental niches; if you ran the history of life over again in slightly different circumstancs, he argues, the human brain almost certainly would not have evolved.

This criticism aside, this is a masterpiece of science popularisation. Warmly recommended.

This book takes the reader on an overview of the history of life (after a review of the formation of earth), culminating in the human mind and then onto speculations about the future of the mind (robotic minds to be sent into the cosmos as immortal explorers). I read this because I was curious about the proto-mammal(s) from which all sprung after the passing of the age of the dinosaurs.

I was immediately linked into a graceful narrative chock full of ideas, from the development of an acute sense of smell - enlarging certain portions of the mammal brain beyond that of its reptilian competitors - for (warm-blooded) night stealth, to the evolution of the human thumb and, finally, the passage from tree to veldt of proto-humans (where they needed the brains to make tools). It is a breathtaking adventure that is also a quick read with ideas that stick. There are also beautiful illustrations that lighten the text and help to engrave it in memory.

Interestingly, I found this book because of a critique by SJ Gould, in which Gould argued that Jastrow's interpretation made the human ascent look inevitable as well as placed man at the top of the animal hierarchy as inherently superior because of the human brain. Not so, argued Gould, who said that every species is the result of their own evolutionary pressures and hence were superior survivors in their original environmental niches; if you ran the history of life over again in slightly different circumstancs, he argues, the human brain almost certainly would not have evolved.

This criticism aside, this is a masterpiece of science popularisation. Warmly recommended.

[Disclaimer: the author of this book was my Ph.D. advisor.]

I have been teaching introductory Artificial Intelligence for about 5 years now at my University, and have tried three different texts. Essentials of Artificial Intelligence was far and away the best. If it were still in print, I would still be using it enthusiastically, in spite of its advancing age.

The chapters are short, clear, and complement excellent explanations with great suggestions for further learning. The coverage is fairly balanced, but with an emphasis on state space search, planning, and scheduling and a lessened emphasis on highly applied topics.

This is not an encyclopedic AI text. I think that is one of its strengths. If you are a college student or professor new to AI, and are looking for a quick but thorough way to come up to speed in the field, this is the book for you.

This is a fantastic book. Don't let its date of publication fool you, the stuff it covers has not changed. This book will provide you with an excellent foundation for future learning.

This is a good book to learn about Estimation of Distribution Algorithms (EDAs or also called DEAs or Iterated DEAs). These algorithms are similar to evolutionary algorithms, but do not use the crossover or mutation operators of evolutionary search. EDAs instead create a probabilistic model of good solutions and use the model to generate new search points. It's a nifty idea and it works.

Most of the chapters of this edited collection were authored or coauthored by the editors. So, algorithms developed by other people do not get a lot of attention. However, the editors (or is it the authors) manage to include chapters on combinatorial, continuous, and discrete optimization.

There is a section on machine learning applications that is OK, but the last chapter on training neural nets with EDAs is very weak (look ma I used this and it worked...). Except for this chapter, the rest of the chapters in this section use careful experiments and statistics to make their points.

Making the source code available would have improved things and would make it easier for people to try these algorithms.

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It is very good book for people who works in structural optimization. It has many nice examples. I will buy it for my library.

This book is perfect to bring the practioner or researcher up to date with the state of the art in the field of GAs. Because each chapter is authored by different contributors, the style varies a lot. However, the book succeeds in its unifying theme. Probably the most useful thing accomplished here is to introduce the research community to the reader so that one can pick up reading directly from the fast evolving literature. Enjoy.

Genetic Design / Optimization is an exciting field. The increasing power of desktop computers are opening up applications in design optimization that heretofore were difficult to solve or optimize.

As a registered professional engineer, I feel comfortable stamping designs derived via evolutionary computation since they must satisfy the physics of the problem in order to be feasible.

I found this book prepares both the practitioner and researcher for this field. The book comes with a CD that contains many web addresses and source code to various application programs which is very valuable resource and time saver.

This is an excellent resource in Genetic Algorithms and Design. I recommend this book highly.

Evolutionary multi-objective optimization is becoming more and more used these days, and most breakthroughs in this subject can be found in these proceedings (proceedings of EMO'03 are even better than EMO'01, the program committee did a wonderful job). These are not the standard technical proceedings whose intended audience are specialists in the field, anyone with a basic knowledge of evolutionary computation can understand these papers. The proceedings begin with short tutorials explaining what EMO is and after that about the first half deals with more theoretical aspects while the second part with applications.

Reading these proceedings like a book is feasible (you may skip some papers you're not interested in) and most of the papers are very well written, so if you're interested in recent advances in EMO, reading these proceedings is almost required. The book by Kalyanmoy Deb (Multi-objective optimization using evolutionary algorithms) is nice but does not provide enough treatment from an applications perspective, but it constitutes a nice textbook to enter the subject of EMO anyway.

Both EMO2001 and 2003 proceedings are "must-read" in evolutionary multi-objective optimization !