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Introduction to Knowledge Systems [Hardcover]

Mark Stefik (Author)
4.7 out of 5 stars  See all reviews (3 customer reviews)


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Book Description

155860166X 978-1558601666 June 15, 1995 1st

Focusing on fundamental scientific and engineering issues, this book communicates the principles of building and using knowledge systems from the conceptual standpoint as well as the practical.
Previous treatments of knowledge systems have focused on applications within a particular field, or on symbol-level representations, such as the use of frame and rule representations. Introduction to Knowledge Systems presents fundamentals of symbol-level representations including
representations for time, space, uncertainty, and vagueness. It also compares the knowledge-level organizations for three common knowledge-intensive tasks: classification, configuration, and diagnosis.


The art of building knowledge systems incorporates computer science theory, programming practice, and psychology. The scope of this book is appropriately broad, ranging from the design of hierarchical search algorithms to techniques for acquiring the task-specific knowledge needed for successful applications.


Each chapter proceeds from concepts to applications, and closes with a brief tour of current research topics and open issues. Readers will come away with a solid foundation that will enable them to create real-world knowledge systems using whatever tools and programming languages are most current and appropriate.


Editorial Reviews

From the Back Cover

Focusing on fundamental scientific and engineering issues, this book communicates the principles of building and using knowledge systems from the conceptual standpoint as well as the practical.
Previous treatments of knowledge systems have focused on applications within a particular field, or on symbol-level representations, such as the use of frame and rule representations. Introduction to Knowledge Systems presents fundamentals of symbol-level representations including
representations for time, space, uncertainty, and vagueness. It also compares the knowledge-level organizations for three common knowledge-intensive tasks: classification, configuration, and diagnosis.


The art of building knowledge systems incorporates computer science theory, programming practice, and psychology. The scope of this book is appropriately broad, ranging from the design of hierarchical search algorithms to techniques for acquiring the task-specific knowledge needed for successful applications.


Each chapter proceeds from concepts to applications, and closes with a brief tour of current research topics and open issues. Readers will come away with a solid foundation that will enable them to create real-world knowledge systems using whatever tools and programming languages are most current and appropriate.

About the Author

By Mark Stefik

Product Details

  • Hardcover: 896 pages
  • Publisher: Morgan Kaufmann; 1st edition (June 15, 1995)
  • Language: English
  • ISBN-10: 155860166X
  • ISBN-13: 978-1558601666
  • Product Dimensions: 9.1 x 7.6 x 1.9 inches
  • Shipping Weight: 3.7 pounds
  • Average Customer Review: 4.7 out of 5 stars  See all reviews (3 customer reviews)
  • Amazon Best Sellers Rank: #272,323 in Books (See Top 100 in Books)

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Average Customer Review
4.7 out of 5 stars (3 customer reviews)
 
 
 
 
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Most Helpful Customer Reviews

3 of 3 people found the following review helpful:
4.0 out of 5 stars Good Book, but heavy on review, June 1, 2001
By 
"jacob1044" (Columbus, OH United States) - See all my reviews
This review is from: Introduction to Knowledge Systems (Hardcover)
This book is very helpful in learning about the concept of knowledge-based systems. I must caution though, that if you are familiar with artificial intelligence there is a lot of material in this book that will act as a review.

When purchasing this book, I was hoping for material on developing a knowledge-based system from scratch. While it does an outstanding job in teaching you the fundamentals (clear through diagnosis and troubleshooting), it seemed to leave me slightly incomplete and in search for additional material on the subject.

It's not a all-in-one book, but this is book is definitely one to consider.

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4 of 5 people found the following review helpful:
5.0 out of 5 stars A classic book on Knowledge systems, June 23, 2000
This review is from: Introduction to Knowledge Systems (Hardcover)
The author gives an exceptional and in-depth view of the topics of search, reasoning with time, space and uncertainty and also on classification. It should make a good text at the graduate-level. The book also gives excellent exercises and open- research problems. A true classic in this area. A must on the desk of every AI practitioner
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3 of 5 people found the following review helpful:
5.0 out of 5 stars This is a useful book for a large audience., March 29, 2000
This review is from: Introduction to Knowledge Systems (Hardcover)
The book covers many aspects of knowledge based systems including knowledge representation, problem solving by search,the marriage between knowledge and software engineering(which is a rising issue at present), temporal and spatial reasoning, uncertainy modeling and many others. The book ends with a chapter on troubleshooting, which attracts many practitioners.The book is rich with good contents. Many examples and illustrations are used to introduce the concepts. I liked chapter 6 very much for its beautiful presentation on probabilistic networks.Chapter 4 and 5 on Reasoning about time and Reasoning about space respectively are also interesting. Readers of different background will find the book extremely useful for its beautiful writing style.
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Inside This Book (learn more)
First Sentence:
By necessary, Newell and Simon meant that an analysis of any system exhibiting general intelligence would show that the system is a physical symbol system. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
system being diagnosed, courier model, backtracking hill climbing, quandaries references, solution tester, indefinite nodes, quandaries section, screening clause, farm road light, gap subproblems, several knowledge systems, building knowledge systems, linear characteristic functions, backtracking constraint satisfaction, candidate tester, simple constraint satisfaction, example knowledge systems, hierarchical match, blind search methods, induced fuzzy set, conjunctive classification, developing knowledge systems, canonical form rules, exit with failure, intermediate abstraction level
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Professor Digit, Normal Observed, Possible Predicted, More Terminology, Peter Rabbit, San Francisco, Specifications Configuration, Candidate Probability Normalized, Computational Linguist, Computer Science Department, Steinway Apartment Building, Did Joe, Digital Equipment Company, Distribution Probability, Following Neapolitan, Probability Assignment Assign, Statement Suppose, Super Bowl, Virus-caused Bacterium-caused Virus-caused Bacterium-caused
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