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 107
... nodes n , and ng be terminal nodes , and let the cost of each k - connector be k . Note that our h function provides ... leaf node of the estimated best partial solution graph to expand . Perhaps it would be efficient to select that leaf ...
... nodes n , and ng be terminal nodes , and let the cost of each k - connector be k . Note that our h function provides ... leaf node of the estimated best partial solution graph to expand . Perhaps it would be efficient to select that leaf ...
Page 115
... nodes at the bottom of the tree . Many of the paths end in terminal nodes at shallower levels , however , and ... leaf nodes of the search graph . The evaluation function measures the " worth " of a leaf node position . The measurement ...
... nodes at the bottom of the tree . Many of the paths end in terminal nodes at shallower levels , however , and ... leaf nodes of the search graph . The evaluation function measures the " worth " of a leaf node position . The measurement ...
Page 201
... leaf node labeled by S. The result is the graph structure shown in Figure 6.3 . The two nodes labeled by S are connected by an arc that we call a match arc . Before applying a rule , an AND / OR graph , such as that of Figure 6.2 ...
... leaf node labeled by S. The result is the graph structure shown in Figure 6.3 . The two nodes labeled by S are connected by an arc that we call a match arc . Before applying a rule , an AND / OR graph , such as that of Figure 6.2 ...
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 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