Artificial Intelligence Books

If you only buy one book on agent systems you could do a whole lot worse than splash out on this thoroughly admirable and admirably thorough work from d'Inverno and Luck. Easy to follow (even if your Z spec is a bit rusty, as mine is), clear and really well set out. The authors have a deep understanding of their subject and a rare ability to communicate their ideas.

There are several books on intelligent agents and multi-agents systems that I've come across, but most are either too broad-ranging and shallow so that they don't actually get to important core issues, or they're too narrow and mathematical for my liking (and for many others). This excellent book somehow manages to pull off the feat of providing a good introduction to agents, while also drilling down to some fascinating and deep issues in multi-agent systems. What's particularly good is that it does two things - it analyses and explains the issues with really clear textual description, and then provides a more formal description (using the Z specification language) that is surprisingly readable.

After providing an introductory chapter, the book presents a "framework" for understanding agent systems (hence the title) in which it brings together various different notions of agents. The chapters cover the framework itself, the different kinds of inter-agent

relationships that arise within it (to get to multi-agent systems), and more complex agents with greater sophistication. There are also a couple of case-study chapters that show how the model can be used to give descriptions of BDI systems and the contract net.

Throughout, the authors provide really good explanations, and then also formal descriptions using Z. Whether or not you buy the claim that Z is the most used industrial formal method, it turns out that despite the mathematical nature of the Z specification, the book as a whole is really very readable. It is worth noting that the level of mathematical description in the book for describing the framework and the systems is pretty close to abstract code descriptions (which is perhaps not surprising given that Z is intended for use for specifying software). With the appendix intro to Z, the book should also be a useful resource for developers wanting to understand exactly what would be involved in building systems.

One of the difficulties I've found when reading about agents is trying to make sense of some very different ideas and systems, and trying to understand how they fit together. This book provides some of the answers. In summary, the book covers some basic agent concepts, and builds them up to describe quite complex multi-agent systems, moving from abstract ideas to descriptions of specific implemented systems, and showing how they come together. It provides an excellent

introduction to agents, and keeps going to address some much deeper issues.

This is a great book about a new topic called "Adaptive Business Intelligence". It is basically about modern Artificial Intelligence (AI) that is used to solve tough real-life optimization and prediction problems. The authors really know what they are talking about and have many years of business and teaching experience. They are also running a software company specializing in Adaptive Business Intelligence.

I was skeptical when I picked up this book and thought to myself that they can't possibly explain all those modern AI techniques without formulas. However, the book is very easy to read and really does cover modern AI techniques without getting into a lot of nitty gritty details (i.e., no formulas). They also have a detailed real-life example that they frequently refer to. The example is very good and I quickly saw how I could use Adaptive Business Intelligence.

The authors explain everything down to earth and on top of that it was fun to read and also reads well. I also liked that they have some references to other books in case you want to dive into a particular topic.

All in all it's a great book that makes modern science easy to understand for busy people. Even non-technical managers like myself will have no problems understanding the different techniques and how they apply to their own business. I highly recommend this book. It's definitely worth reading!

PS: I borrowed the book to my sister who studies IT in California and she told me that it's a great introduction to modern AI. She actually never returned the book since she kept as a reference for one of her classes.

This book shows its age in some areas, such as the comments about the feasibility of running SDM neural networks on then-contemporary hardware. Still, it provides good coverage of several non-MLP neural networks (such as CPM, RCE, SDM and RBF neural networks [PNN/GRNN]). Also covered to some degree are fuzzy logic and chaotic systems (such as Freeman's olfactory cortex model). The book's language is very easy to follow, yet proficient programmers should be able to reproduce any of the neural networks described.

Pros:
-Nice variety of "alternative" neural network architectures
-Well written

Cons:
-Getting a bit old (having been published in 1993)

The Design Informatics community of architects and civilengineers will find this book of reference useful to their research.

Although the papers are categorized into "theory" and "applications", all papers are quite practice-oriented. The first 23 "theoretical" papers present various sub-fields of EC while still providing good entries to the applications of these sub-fields. The other 17 papers focus on the application of EC techniques on very specific problems, be they combinatorial or mostly applied.

The papers are quite well written so that reading from cover to cover is possible without getting a headache...A very good book to get an idea of the applications of EC on real problems.

i like solve any problem with fuzzy thought. i like this method. i like every one think about this subject.

I read a lot of papers from this book. And I read twice and twice a lot of them. This is a book to the professional and to the novice in data mining (d.m.). In it you take a general view from data mining and the particular one you pretend because you can see papers from "what is d.m.", "how to use d.m.", "d.m. techniques", "d.m. and visualization", "applications from d.m.", "d.m. tools", "advances in d.m.", "the future and d.m.".

I find this book very informative yet uptodate if you want to know what are the recent advances in machine learning field of research. The topics presented are very insteresting and easy to read and understand.

This book is an excellent reference book on evolutionary design of intelligent agents. It is a welcome addition to the growing body of literature on intelligent agents in general, and evolutionary approaches to synthesis of such agents in particular. One of the editors, Professor Honavar, as amply demonstrated by his other publications, has the rare ability to interrelate an amazing diversity of perspectives on Artificial Intelligence - from symbolic methods to neural networks and evolutionary apporaches, to uncover the shared principles and common foundations, and show how different paradigms can be brought together in synergistic ways to advance our ability to design and analyze intelligent systems. This book includes chapters written by some of the leading experts in the field. The first chapter provides an excellent introduction to the topic. The rest of the chapters provide a veritable feast of material ranging from design of sensory systems for robots to current research on design of software agents. Most of the chapters, although they describe some of the latest research in this area, are written in a style that is accessible to even an interested layperson. (My 15 year old daughter has enjoyed reading several chapters). The book should be especially useful as a reference to graduate students and researchers interested in artificial intelligence, machine learning, evolutionary computation, software agents, and computational evolutionary biology. It should also be useful as supplementary reading for a graduate or upper level undergraduate course on evolutionary computation, computational intelligence, or a related area or as a primary text for a seminar course. I recommend this book enthusiastically to everyone who is interested in the science and engineering of intelligent agents.

Sometimes I read because I want to feel happiness, or sadness, or that mix of fearful excitement and untainted friendship that makes you and your childhood buddies return to your hometown to fight an epic battle against a shapeshifting clown. Yes, sometimes I long for a world less unreal than the one we are living in. Other times I want to be part of a history that never was, or of a future that never will be. And sometimes I'm just curious how life looks through the eyes of a sex-addict med-school dropout who is a direct descendant of Jesus Christ.

These longings are quickly satisfied and, once I'm halfway through a book and too tired to read on, there is little incentive for me to pick it up again. The shelves of my local library are filled with millions of pages depicting everyday life, stereotyped, dramatized, neatly packaged and sent to a world slightly differing in time, space and causality. Because that's what we want, and, web designers and capitalists agree, what we want is what we get.

Some writers, however, take you to places that are so much larger than everyday life that you cannot but notice the peculiarities of the context you live in. The patterns inherent in the seemingly incessant flow of human life suddenly stick out in all their lovely absurdity and childish predictability. If you are going to read only two stories of this kind in your life and if you are already done with Permutation City, do yourself a favor and make Simon Funk's After Life your next Saturday evening read.