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 19
For the 8 - puzzle and certain other problems , we can easily identify elements of
the problem that correspond to these three components . These elements are the
problem states , moves , and goal . In the 8 - puzzle , each tile configuration is a ...
For the 8 - puzzle and certain other problems , we can easily identify elements of
the problem that correspond to these three components . These elements are the
problem states , moves , and goal . In the 8 - puzzle , each tile configuration is a ...
Page 20
The 8 - puzzle is conveniently interpreted as having the following four moves :
Move empty space ( blank ) to the left , move blank up , move blank to the right ,
and move blank down . These moves are modeled by production rules that
operate ...
The 8 - puzzle is conveniently interpreted as having the following four moves :
Move empty space ( blank ) to the left , move blank up , move blank to the right ,
and move blank down . These moves are modeled by production rules that
operate ...
Page 92
For example , we can use Figure 2.12 to calculate that the use of the evaluation
function f = 8 + P +39 results in a B value equal to 1.08 for the 8 - puzzle problem
illustrated in Figure 2.9 . Suppose we wanted to estimate how many nodes would
...
For example , we can use Figure 2.12 to calculate that the use of the evaluation
function f = 8 + P +39 results in a B value equal to 1.08 for the 8 - puzzle problem
illustrated in Figure 2.9 . Suppose we wanted to estimate how many nodes would
...
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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 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 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 search tree selected sequence shown in Figure simple solution graph solve specify statement step STRIPS structure subgoal substitutions successors Suppose symbols termination unifying unit universal variables