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 63
... node has at most one parent . A node in the tree having no parent is called a root node . A node in the tree having no successors is called a tip node . We say that the root node is of depth zero . The depth of any other node in the ...
... node has at most one parent . A node in the tree having no parent is called a root node . A node in the tree having no successors is called a tip node . We say that the root node is of depth zero . The depth of any other node in the ...
Page 67
... node 1 is expanded , its single successor , node 2 , is generated . But node 2 , with parent node 3 in the search tree , had previously been generated , and node 2 is also on CLOSED with successor nodes 4 and 5. Note , however , that ...
... node 1 is expanded , its single successor , node 2 , is generated . But node 2 , with parent node 3 in the search tree , had previously been generated , and node 2 is also on CLOSED with successor nodes 4 and 5. Note , however , that ...
Page 122
... nodes B , C , and D can in no way affect what will turn out to be MAX's best first move . In this example , the search savings depended on the fact that node A represented a win for MIN . The same kind of savings can be achieved ...
... nodes B , C , and D can in no way affect what will turn out to be MAX's best first move . In this example , the search savings depended on the fact that node A represented a win for MIN . The same kind of savings can be achieved ...
Contents
PROLOGUE | 1 |
PRODUCTION SYSTEMS AND AI | 17 |
SEARCH STRATEGIES FOR | 53 |
Copyright | |
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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 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 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