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. |
From inside the book
Results 1-3 of 90
Page 80
Thus , A , always expands at least as many nodes as does the more informed Ag
. We prove this result using induction on the depth of a node in the A , search tree
at termination . First , we prove that if A , expands a node n having zero depth in ...
Thus , A , always expands at least as many nodes as does the more informed Ag
. We prove this result using induction on the depth of a node in the A , search tree
at termination . First , we prove that if A , expands a node n having zero depth in ...
Page 249
We elect to prove this subgoal by proving C2 while having available ( only for use
on C2 and its descendant subgoals ) the B - rule ( A → C1 ) . ( This manner of
treating an implicational goal was discussed earlier . Now , however , rather than
...
We elect to prove this subgoal by proving C2 while having available ( only for use
on C2 and its descendant subgoals ) the B - rule ( A → C1 ) . ( This manner of
treating an implicational goal was discussed earlier . Now , however , rather than
...
Page 411
28 were marked simply as defaults , for example , we would be at an impasse :
We could prove that Clyde was gray only if we could not prove that he was any
other color . However , we could prove that he was another color , namely , white
, if ...
28 were marked simply as defaults , for example , we would be at an impasse :
We could prove that Clyde was gray only if we could not prove that he was any
other color . However , we could prove that he was another color , namely , white
, if ...
What people are saying - Write a review
We haven't found any reviews in the usual places.
Contents
PROLOGUE | 1 |
PRODUCTION SYSTEMS AND AI | 17 |
SEARCH STRATEGIES FOR | 53 |
Copyright | |
9 other sections not shown
Other editions - View all
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 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
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 |