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 75
goal node . That is , f ( n ) is an estimate of the cost of a minimal cost path
constrained to go through node n . That node on OPEN having the smallest value
of f is then the node estimated to impose the least severe constraint ; hence it is ...
goal node . That is , f ( n ) is an estimate of the cost of a minimal cost path
constrained to go through node n . That node on OPEN having the smallest value
of f is then the node estimated to impose the least severe constraint ; hence it is ...
Page 78
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 ...
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 ...
Page 254
In the initial graphs , match edges between the fact and goal graphs must be
between leaf nodes . After the graphs are extended by B - rule and F - rule
applications , the matches might occur at any literal node . After all possible
matches ...
In the initial graphs , match edges between the fact and goal graphs must be
between leaf nodes . After the graphs are extended by B - rule and F - rule
applications , the matches might occur at any literal node . After all possible
matches ...
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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 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 |