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 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 ƒ is then the node estimated to impose the least severe constraint ; hence ...
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 ƒ is then the node estimated to impose the least severe constraint ; hence ...
Page 78
Thus , RESULT 3 : If there is a path from s to a goal node , A * terminates . RESULT 3 has an interesting corollary , namely , that any node , n , on OPEN with f ( n ) < ƒ * ( s ) will eventually be selected for expansion by A * .
Thus , RESULT 3 : If there is a path from s to a goal node , A * terminates . RESULT 3 has an interesting corollary , namely , that any node , n , on OPEN with f ( n ) < ƒ * ( s ) will eventually be selected for expansion by A * .
Page 204
Goal Nodes C G C D E G Α B A Rules : A = CAD B⇒ ENG B Fact ( A V B ) Fig . ... When one of the goal literals matches a literal labeling a literal node , n , of the graph , we add a new descendant of node n , labeled by the matching ...
Goal Nodes C G C D E G Α B A Rules : A = CAD B⇒ ENG B Fact ( A V B ) Fig . ... When one of the goal literals matches a literal labeling a literal node , n , of the graph , we add a new descendant of node n , labeled by the matching ...
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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 Intelligence 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 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 |