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Message from Dean - May 8th 2007
I am currently testing out a new version of the APF Bridge Component - If you notice any errors within this demo store please drop me a line.
List Price: $90.00Amazon.com's Price: $72.01 You Save: $17.99 (20%)Prices subject to change.
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Binding: Hardcover
Dewey Decimal Number: 004.21
EAN: 9780262061575
ISBN: 0262061570
Label: The MIT Press
Manufacturer: The MIT Press
Number Of Items: 1
Number Of Pages: 716
Publication Date: November 19, 1993
Publisher: The MIT Press
Studio: The MIT Press
Alternate Versions: Click to Display
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Editorial Review:
Product Description:
For nearly two decades, Kenneth Forbus and Johan de Kleer have accumulated a substantial body of knowledge about the principles and practice of creating problem solvers. In some cases they are the inventors of the ideas or techniques described, and in others, participants in their development.
Building Problem Solvers communicates this knowledge in a focused, cohesive manner. It is unique among standard artificial intelligence texts in combining science and engineering, theory and craft to describe the construction of AI reasoning systems, and it includes code illustrating the ideas.
After working through Building Problem Solvers, readers should have a deep understanding of pattern directed inference systems, constraint languages, and truth maintenance systems. The diligent reader will have worked through several substantial examples, including systems that perform symbolic algebra, natural deduction, resolution, qualitative reasoning, planning, diagnosis, scene analysis, and temporal reasoning.
Kenneth D. Forbus is Professor of Computer Science and Education at the Institute for Learning Sciences, Northwestern University. Johan de Kleer directs the Systems and Practices Laboratory at the Xerox Palo Alto Research Center.
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Building Problem Solvers is a very hands-on introduction to AI systems in general, and truth-maintenance systems in particular. All the systems discussed come fully implemented. In this regard, the book can be viewed as an accessible and detailed discussion of the code. For this reason, the best way to read any chapter is to scan through it first, then, carefully go through it again with the source code in sight.
The authors were PhD students of Gerry Sussman at MIT. Thus, this book is a great way to learn about the classic AI systems and techniques devised and refined at the MIT AI lab.
If you get serious about the book, you'll want to try out a few of the exercises. I found that the exercises are invariably insightful, though I wish some included implementation hints, because elegant solutions are often far from obvious.
In short, I highly recommend this book if you're looking to build some problem solver using proven AI techniques.
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This book takes you on a journey through the techniques that have been used to build problem solvers, ranging from classical search techniques to Assumptive Truth Maintenance. It is very easy to read, despite the high level of technical detail. The LISP code that accompanies the book is well documented, easy to understand, and it works. It is a must have for anyone who truly wants to understand problem solving techniques.
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