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
Results 1-5 of 33
... description, of them. This description is then used as the global database of a production system. For the 8-puzzle, a straightforward description is a 3 X 3 array or matrix of numbers. The initial ... state. Virtually any kind of data ...
... state descriptions in the appropriate manner. The rules each have preconditions that must be satisfied by a state description ... initial state into a goal state. The problem goal condition forms the basis for the termination condition of the ...
... state description, the negative of the number of tiles “out of place,” as compared to the goal state description. For example, the value of this function for the initial state in Figure 1.1 is –4, and the value for the goal state is 0 ...
... initial state description lowers the value of our hill-climbing function. In this case the initial state description is at a local (but not a global) maximum of the function. Other types of hill-climbing frustrations also occur: The ...
... state description that already occurs on the path back to the initial state description, (b) whenever we have applied an arbitrarily set number of rules without having generated a goal state description, or (c) whenever there are no ...
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 |