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 67
The solid nodes are on CLOSED , and the other nodes are on OPEN at the time
the algorithm selects node 1 for expansion . ( We assume unit arc costs . ) When
node 1 is expanded , its single successor , node 2 , is generated . But node 2 ...
The solid nodes are on CLOSED , and the other nodes are on OPEN at the time
the algorithm selects node 1 for expansion . ( We assume unit arc costs . ) When
node 1 is expanded , its single successor , node 2 , is generated . But node 2 ...
Page 76
( 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 . ) Notice that this definition implies g ( n ) > g * ( n ) . For the estimate h ( n )
...
( 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 . ) Notice that this definition implies g ( n ) > g * ( n ) . For the estimate h ( n )
...
Page 80
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 .
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 .
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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 efficient evaluation example expression F-rule fact Figure formula function given global database goal 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 |