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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To take a specific example, suppose the facts are the personnel records of a large corporation. Example items in such a database might be representations for such facts as “Joe Smith works in the Purchasing Department,” “Joe Smith was ...
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 city.
(For example, the number of cities would be a measure of the size of a traveling salesman problem.) Thus, problem difficulty may grow linearly, polynomially, or exponentially, for example, with problem size. The time taken by the best ...
The scene's category could then serve as its description. For example, perhaps a detector could be built that could test a scene to see if it belonged to the category “A hill with a tree on top ...
Then the goal would be to achieve any state satisfying this condition. Such a condition implicitly defines some set of goal states. For example, in the 8-puzzle, we might want to achieve any tile configuration for which the ...
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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 |
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Bibliographic information
| Title | Principles of Artificial Intelligence |
| Author | Nils J. Nilsson |
| Edition | reprint |
| Publisher | Morgan Kaufmann, 2014 |
| ISBN | 1483295869, 9781483295862 |
| Length | 476 pages |
| Subjects | › Computers / General |
| Export Citation | BiBTeX EndNote RefMan |