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 Intelligence evolved 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 66
In this case , each member of M is added to OPEN and is installed in the search
tree as a successor of n . The search graph is the search tree throughout the
execution of the algorithm , and there is no need to change parents of the nodes
in T.
In this case , each member of M is added to OPEN and is installed in the search
tree as a successor of n . The search graph is the search tree throughout the
execution of the algorithm , and there is no need to change parents of the nodes
in T.
Page 67
graph and search tree shown in Figure 2.4 . 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 ...
graph and search tree shown in Figure 2.4 . 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 ...
Page 80
It is reasonable to say that A * with h ( n ) = Win ) is more informed than breadth -
first search , which uses h ( n ) = 0 . We would ... First , we prove that if A ,
expands a node n having zero depth in its search tree , then so will A ,. But , in
this case ...
It is reasonable to say that A * with h ( n ) = Win ) is more informed than breadth -
first search , which uses h ( n ) = 0 . We would ... First , we prove that if A ,
expands a node n having zero depth in its search tree , then so will A ,. But , in
this case ...
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Contents
PROLOGUE | 1 |
PRODUCTION SYSTEMS AND AI | 17 |
SEARCH STRATEGIES FOR | 53 |
Copyright | |
10 other sections not shown
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Common terms and phrases
achieve actions algorithm AND/OR graph answer applied arcs assertions assume attempt backtracking backward block called chapter clause CLEAR(C complete component condition consider consistent contains control strategy corresponding cost database Deleters described direction discussed efficient evaluation example expanded 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 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 search tree selected sequence shown in Figure simple solution graph solve specify statement step STRIPS structure subgoal substitutions successors Suppose symbols termination unifying unit universal variables
Bibliographic information
| Title | Principles of Artificial Intelligence |
| Author | Nils J. Nilsson |
| Edition | illustrated, reprint |
| Publisher | Morgan Kaufmann Publishers, 1986 |
| Original from | the University of Virginia |
| Digitized | 27 Jul 2009 |
| ISBN | 0934613109, 9780934613101 |
| Length | 476 pages |
| Subjects | › Reference / General |
| Export Citation | BiBTeX EndNote RefMan |