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 208
The notions of a consistent set of substitutions and a unifying composition of substitutions are defined as follows . Suppose we have a set of substitutions , { u1,2 , ... , un } . Each u , is , in turn , a set of pairs : u1 = { t1 ...
The notions of a consistent set of substitutions and a unifying composition of substitutions are defined as follows . Suppose we have a set of substitutions , { u1,2 , ... , un } . Each u , is , in turn , a set of pairs : u1 = { t1 ...
Page 217
The fact nodes are shown double - boxed , and rule applications are labeled by the rule number . To verify the consistency of this solution graph , we compute the unifying composition of all of the substitutions labeling the match arcs ...
The fact nodes are shown double - boxed , and rule applications are labeled by the rule number . To verify the consistency of this solution graph , we compute the unifying composition of all of the substitutions labeling the match arcs ...
Page 239
For example , in Figure 6.24 , the literals S ( x , B ) and ~ S ( A , y ) are in two different partial solution graphs and their predicates unify with mgu { A / x , B / y } . Applying this mgu to S ( x , B ) yields S ( A , B ) ...
For example , in Figure 6.24 , the literals S ( x , B ) and ~ S ( A , y ) are in two different partial solution graphs and their predicates unify with mgu { A / x , B / y } . Applying this mgu to S ( x , B ) yields S ( A , B ) ...
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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 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 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 |