Principles of Artificial IntelligenceA 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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Pertinent references are briefly discussed at the end of every chapter. These citations should provide the interested student with adequate entry points to much of the most important literature in the field. I look forward someday to ...
Many of these problems can be attacked by the methods discussed in this book. A classical example is the traveling salesman's problem, where the problem is to find a minimum distance tour, starting at one of several cities, ...
Systems for proving theorems using resolution are discussed in chapter 5. We indicate how several different kinds of problems can be posed as theorem-proving problems. Chapter 6 examines some of the inadequacies of simple resolution ...
[See also the discussion by Constable (1979) in the same volume.] The formal verification of properties of programs was discussed early in the history of computing by Goldstine and von Neumann (1947) and by Turing (1950).
... refers to more specialized types of computational systems than those discussed in this book. Production systems derive from a computational formalism proposed by Post (1943) that was based on string replacement rules.
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Contents
1 | |
17 | |
53 | |
CHAPTER 3 SEARCH STRATEGIES FOR DECOMPOSABLE PRODUCTION SYSTEMS | 99 |
CHAPTER 4 THE PREDICATE CALCULUS IN AI | 131 |
CHAPTER 5 RESOLUTION REFUTATION SYSTEMS | 161 |
CHAPTER 6 RULEBASED DEDUCTION SYSTEMS | 193 |
CHAPTER 7 BASIC PLANGENERATING SYSTEMS | 275 |
CHAPTER 8 ADVANCED PLANGENERATING SYSTEMS | 321 |
CHAPTER 9 STRUCTURED OBJECT REPRESENTATIONS | 361 |
PROSPECTUS | 417 |
BIBLIOGRAPHY | 429 |
AUTHOR INDEX | 467 |
SUBJECT INDEX | 471 |