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. |
From inside the book
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... 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 revising this book—to correct its ...
... 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, visiting each city precisely once, and returning to the starting ...
... 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 systems and describes some alternatives, called rule ...
... discussed early in the history of computing by Goldstine and von Neumann (1947) and by Turing (1950). Program verification was mentioned by McCarthy (1962) as one of the applications of a proposed mathematical science of computation ...
... discussed in this book. Production systems derive from a computational formalism proposed by Post (1943) that was based on string replacement rules. The closely related idea of a Markov algorithm [Markov (1954), Galler and Perlis (1970)] ...
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 |