Principles of Artificial Intelligence
A classic introduction to artificial intelligence intended to bridge the gap between theory and practice, Principles of Artificial Intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval. Rather than focusing on the subject matter of the applications, the book is organized around general computational concepts involving the kinds of data structures used, the types of operations performed on the data structures, and the properties of the control strategies used.
Principles of Artificial Intelligenceevolved from the author's courses and seminars at Stanford University and University of Massachusetts, Amherst, and is suitable for text use in a senior or graduate AI course, or for individual study.
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... Graph-search Procedures 68 Heuristic Graph-search Procedures 72 Related Algorithms 88 Measures of Performance 91 Bibliographical and Historical Remarks ...
... includes several components, one of which is a rule-based system for synthesizing programs from descriptions of abstract algorithms [Barstow (1979)].
THE BASIC PROCEDURE The basic production system algorithm for solving a problem like the 8-puzzle can be written in nondeterministic form as follows: ...
The closely related idea of a Markov algorithm [Markov (1954), Galler and Perlis (1970)] involves imposing an order on the replacement rules and using this ...
Altogether, the algorithm backtracks 22 times before finding a solution; even the very first rule applied must ultimately be taken back.
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CHAPTER 3 SEARCH STRATEGIES FOR DECOMPOSABLE PRODUCTION SYSTEMS
CHAPTER 4 THE PREDICATE CALCULUS IN AI
CHAPTER 5 RESOLUTION REFUTATION SYSTEMS
CHAPTER 6 RULEBASED DEDUCTION SYSTEMS
CHAPTER 7 BASIC PLANGENERATING SYSTEMS
CHAPTER 8 ADVANCED PLANGENERATING SYSTEMS
CHAPTER 9 STRUCTURED OBJECT REPRESENTATIONS