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 107
A breadth - first algorithm can be obtained from AO * by using h = 0 . Because
such an h function satisfies the monotone restriction ( and is a lower bound on h *
) , the breadth - first algorithm using it is admissible . As an example of the use of
...
A breadth - first algorithm can be obtained from AO * by using h = 0 . Because
such an h function satisfies the monotone restriction ( and is a lower bound on h *
) , the breadth - first algorithm using it is admissible . As an example of the use of
...
Page 151
With { li } = { P ( x , f ( A ) ] } and { m } = { ~ P ( 2 , f ( A ) ] } , we obtain the resolvent
P [ 2 , f ( y ) ] V ~ Q ( z ) VQ ( y ) . ... Three of these are obtained by resolving on P
and one by resolving on Q . It is not difficult to show that resolution is a sound ...
With { li } = { P ( x , f ( A ) ] } and { m } = { ~ P ( 2 , f ( A ) ] } , we obtain the resolvent
P [ 2 , f ( y ) ] V ~ Q ( z ) VQ ( y ) . ... Three of these are obtained by resolving on P
and one by resolving on Q . It is not difficult to show that resolution is a sound ...
Page 176
The resolution refutation is obtained in the usual manner , by first . negating the
wff to be proved , adding this negation to the sets , converting all of the members
of this enlarged set to clause form , and then , by resolution , showing that this set
...
The resolution refutation is obtained in the usual manner , by first . negating the
wff to be proved , adding this negation to the sets , converting all of the members
of this enlarged set to clause form , and then , by resolution , showing that this set
...
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Contents
PROLOGUE | 1 |
PRODUCTION SYSTEMS AND AI | 17 |
SEARCH STRATEGIES FOR | 53 |
Copyright | |
8 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 evaluation example expression F-rule fact Figure formula function given global database 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 |