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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Results 1-5 of 7
If the result is saved, then the goal test in step 5 need only look up the result
instead of repeating a possibly costly computation.) The depth-first procedure
generates new databases in an order similar to that generated by an uninformed
...
The use of an evaluation function that fails to recognize the true promise of some
nodes can result in nonminimal ... the promise of all nodes (such as the
evaluation function yielding breadth-first search) results in expansion of too many
nodes.
Thus, we have: RESULT 2: At any time before A* terminates, there exists on
OPEN a node n' that is on an optimal path from s to a goal node, with f(n) < f*(s).
Combining this result with our previous argument, that even the smallest f values
of ...
Now A* selected n before termination, so at this time (by RESULT 2) we know
that there existed on OPEN some node n' on an optimal path from s to a goal with
f(n) = f*(s). If n = n', our result is established. Otherwise, we know that A* chose to
...
f(n2) = g(n2) + h (ng) = g”(n2) + h(n,) (RESULT 7) g*(n1) + c (n1, m2) + h (n2) – g(
n1) + c (n1, n.2) + h (ng) (RESULT 7) Since the monotone restriction implies c(n1,
m2) + h (n2) > h (n1), we have f(n2) > g(n1) + h (n1) = f(n1). Since this fact is ...
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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 |