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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Altogether, the algorithm backtracks 22 times before finding a solution; even the
very first rule applied must ultimately be taken back. A more efficient algorithm (
with less backtracking) can be obtained if we use a more informed rule ordering.
Note that the recursive algorithm does not remember all databases that it visited
previously. Backtracking involves “forgetting” all databases whose paths lead to
failures. The algorithm remembers only those databases on the current path back
...
The dark arrows along certain arcs in this search graph are the pointers that
define parents of nodes in the search tree. The solid nodes are on CLOSED, and
the other nodes are on OPEN at the time the algorithm selects node 1 for
expansion.
summing the arc costs encountered while tracing the pointers from n to s. (This
path is the lowest cost path from s to n found so far by the search algorithm. The
value of g(n) for certain nodes may decrease if the search tree is altered in step 7.
) ...
We would expect intuitively that the more informed algorithm typically would need
to expand fewer nodes to find a minimal cost path. In the case of the 8-puzzle,
this observation is supported by comparing Figure 2.7 with Figure 2.8. Of course
...
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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 / Data Processing Computers / Expert Systems Computers / General Computers / Intelligence (AI) & Semantics |
| Export Citation | BiBTeX EndNote RefMan |