Artificial Intelligence Books

There has always been an interface between man and machine. In the earliest cave man days it was learned that fastening a rock to the end of a stick made a club, or if sharpened an axe. But it is only in the 1900's that the man machine interface began to be really complex. And it was only in the late 1900's that serious work began on how best to handle this interface.

This book was written by pioneers in the field and offers a principled approach to studying human work with complex technology. It describes a conceptual framework for analysis and concrete methods for the design of man machine interfaces where the amount of information to be transfered between man and machine can be analyzed and structured for its best effect.

This book takes a rather theoretical approach to the problem giving few concrete examples. It seems destined to become a clasic in the field.

This book teaches you about how computer "scientists" have tried to make a program that can beat the world's best chess player. It talks about programs from the '50s to '90s with moves and diagrams about the games. It has pictures of the authors of the programs such as Robert Hyatt programmer of Cray Blitz or Dave Kittinger author of WChess. It is highly detailed and shows you how a computer chess program plays. If you get this book be prepared for a real lesson on computer chess. You'd be surprised how chess programs have gone from losing to a complete beginner to beating a world champion!

The author describes some of the classic problems involved
in formulating a knowledge base. For instance, the choice of
a domain is difficult where the problem is too complex or
experts disagree. In addition, acceptance is difficult to
obtain universally across many organizations. Uncertainty
is always a factor in formulating an expert system. Updating the
knowledge base is another issue because the current applicability
of facts and data will always be a critical concern. Every
expert system has some limitations. These need to be set forth.
The development and refinement of algorithms involves the
constant induction and perfection of rules and rule structures.
In a structured approach to induction, it is critical to identify
problems and attributes while optimizing the rule structures.

This book makes a detailed examination of the critical problem of interestingness in data mining and knowledge discovery. The book surveys relevant work and proceeds to define a framework for measuring interestingness. The techniques discussed are applicable and transferable to real world data mining tasks where the volume of discovered knowledge is often large enough in itself to require further sorting. The book combines topical discussion, experimental evaluation and mathematical formalism in a concise and professional way. In addition, the relevant work of others is cited throughout. This book is well suited to data mining practitioners who develop/and or tweak their own algorithms and are looking to enhance their expertise in this area.

I have found this book very informative and interesting. The book includes many interesting ideas, research results, and practical aspects of knowledge discovery processes for business information systems. The editors invited very fine group of authors, and also actively participated in presenting their own findings. Researchers, educators, and students can learn a lot from this book.

It is rare that a book appears which presents a solution to a major outstanding problem. It is even rarer when a book presents a solution which is both theoretically sound and undeniably practical. But this is such a book. The frame problem, which went unnoticed for the first 70 years of logic, and unsolved for the next 30, here gets a solution.

This book is the distillation of Reiter's lifeswork. Unlike many other such books, however, it is not a mere cut-n-paste of conference papers and tech reports. Instead of being a pastiche, it is rather a continuous, sustained argument, persuasively and clearly presented. A masterpiece.

There is something here to infuriate everybody. Those who think that AI shouldn't be using logic-based methods will be infuriated because Reiter has here, for the first time, made them work. Those who think that first-order logic is the only true logic will be infuriated, because Reiter shows how 2nd order logic is key. Those who think that AI is too theoretical will be infuriated because this is a practical book. Those who think that AI shouldn't worry about being practical will be infuriated, because Reiter has shown that the best theory leads to the most practical solution. Those who are LISP fans will be infuriated because his methods lead inexorably to PROLOG. Read the book, be infuriated by it, and humbly learn from it. The master speaks.

If you've made it to this web page or this far in the review, you have to buy and read this book :-)

As a woman who has learnt how to program I wish this had been one of my first year texts. So many of my textbooks have been on procedural knowledge but this has explained in declarative terms the way it works. Made all the difference. Worth every penny.

This proceedings is a 1,333 page compendium of technical papers each of which is based on the concept of Artificial Intelligence. Only papers which "...included significant intelligent systems content..." and which excluded purely classical methods were published. About 180 separate papers in the English language appear in a standard format: introduction, presentation, conclusion, references. The authors are from universities and research organizations throughout the world.

The subjects presented are unlimited in scope and describe applications in nearly every imaginable field. However, the single common thread is the inclusion of higher mathematics to rationalize the procedure. Whether a paper deals with objects, actions, or information, or a combination of these, the authors customarily provide a mathematical formulation. In many cases, the authors conducted an experiment to demonstrate or test the application in a real-life setting. Computing systems were usually employed to help generate solutions.

This was not intended to be read like a novel from chapter to chapter, nor can a typical scientist or engineer be expected to have the expertise to fully comprehend every paper. It is essentially a research reference on current applications in Artificial Intelligence which incorporates extensive reference lists, with website and email addresses, affording pursuit of the concepts in depth.

It is highly recommended to scientists, engineers, and research personnel with very strong mathematical skills as a resource publication.

Leonard C. Silvern
Systems Engineering Laboratories
Clarkdale, AZ

Recommendation:
For graduate students
Basic:
This book is a set of papers about problem solving with Artificial Nueral Networks. So, you have to be well fimiliarized with the Neural Network concept. All these papers shows an approach to solve specific problems when creting an infernce machine related with a data base(Knowledge Base).
Intermediate:
Some knowledge on labeled graphs, and calculus can't be avoid. With this book you can : explore the several knowledge representation, introduce them into an Artificial Neural Network with testing and learning periods, and get the new rules generated once learning period ends.
Advance:
If you are developing an Expert System or a Knowledge-Based system you can't avoid use the Human Expert when you are designig the Knowledge Base. But, the expert would be only needed as a example provider for training and not as rule provider by using these book. So, the Artificial Neural Network works as the rule generator and inference engine.

A must for any fan of The Goal