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 29
The processes required to represent problems initially and to improve given
representations are still poorly understood . It seems that desirable shifts in a
problem ' s representation depend on experience gained in attempts to solve it in
a ...
The processes required to represent problems initially and to improve given
representations are still poorly understood . It seems that desirable shifts in a
problem ' s representation depend on experience gained in attempts to solve it in
a ...
Page 361
CHAPTER 9 STRUCTURED OBJECT REPRESENTATIONS As we discussed in
chapter 4 , there are many ways to represent a body of knowledge in the
predicate calculus . The appropriateness of a representation depends on the
application ...
CHAPTER 9 STRUCTURED OBJECT REPRESENTATIONS As we discussed in
chapter 4 , there are many ways to represent a body of knowledge in the
predicate calculus . The appropriateness of a representation depends on the
application ...
Page 370
A GRAPHICAL REPRESENTATION : SEMANTIC NETWORKS The binary -
predicate version of predicate calculus ... ( namely , the constant and variable
symbols and the functional expressions ) can be represented by nodes of a
graph .
A GRAPHICAL REPRESENTATION : SEMANTIC NETWORKS The binary -
predicate version of predicate calculus ... ( namely , the constant and variable
symbols and the functional expressions ) can be represented by nodes of a
graph .
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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 |