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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Page 34
The computational effort is the same for both directions . There are occasions ,
however , when it is more efficient to solve a problem in one direction rather than
the other . Suppose , for example , that there were a large number of explicit goal
...
The computational effort is the same for both directions . There are occasions ,
however , when it is more efficient to solve a problem in one direction rather than
the other . Suppose , for example , that there were a large number of explicit goal
...
Page 80
Of course , merely because one algorithm expands fewer nodes than another
does not imply that it is more efficient . The more informed algorithm may indeed
have to make more costly computations , which would destroy efficiency .
Of course , merely because one algorithm expands fewer nodes than another
does not imply that it is more efficient . The more informed algorithm may indeed
have to make more costly computations , which would destroy efficiency .
Page 392
In any given reasoning problem , efficiency considerations demand that we do
not derive all of these facts about Clyde explicitly . Similar efficiency problems
arise when delineations in a taxonomic hierarchy are used to reason backward .
In any given reasoning problem , efficiency considerations demand that we do
not derive all of these facts about Clyde explicitly . Similar efficiency problems
arise when delineations in a taxonomic hierarchy are used to reason backward .
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Contents
PROLOGUE | 1 |
PRODUCTION SYSTEMS AND AI | 17 |
SEARCH STRATEGIES FOR | 53 |
Copyright | |
9 other sections not shown
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Common terms and phrases
achieve actions algorithm AND/OR graph answer applied arcs Artificial Intelligence assume attempt backtracking backward block called chapter clause CLEAR(C complete component condition consider consistent contains control strategy corresponding cost database deduction Deleters described direction discussed efficient evaluation example expression F-rule fact Figure formula function given goal goal node goal stack goal wff HANDEMPTY heuristic important initial involves JOHN knowledge labeled language literals logic match methods move namely node Note obtained occur ONTABLE(A operation path possible precondition predicate calculus problem procedure production system proof prove quantified reasoning refutation represent representation resolution result robot rule satisfied selected sequence shown in Figure simple solution graph solve specify statement step STRIPS structure subgoal substitutions successors Suppose symbols termination theorem unifying unit University variables
Bibliographic information
| Title | Principles of Artificial Intelligence Symbolic computation |
| Author | Nils J. Nilsson |
| Edition | illustrated |
| Publisher | Tioga Publishing Company, 1980 |
| Original from | the University of Michigan |
| Digitized | 15 Nov 2007 |
| ISBN | 0935382011, 9780935382013 |
| Length | 476 pages |
| Export Citation | BiBTeX EndNote RefMan |