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 176
can be answered if we first prove that the wff ( 3x ) AT ( FIDO , x ) logically follows
from S and then find an instance of the ... containing an existential quantifier such
that the existentially quantified variable represents an answer to the question .
can be answered if we first prove that the wff ( 3x ) AT ( FIDO , x ) logically follows
from S and then find an instance of the ... containing an existential quantifier such
that the existentially quantified variable represents an answer to the question .
Page 178
We note that the answer statement has a form similar to that of the goal wff . In
this case , the only difference is that we have a constant ( the answer ) in the
answer statement in the place of the existentially quantified variable in the goal
wff .
We note that the answer statement has a form similar to that of the goal wff . In
this case , the only difference is that we have a constant ( the answer ) in the
answer statement in the place of the existentially quantified variable in the goal
wff .
Page 188
In conclusion , the steps of the answer extraction process can be summarized as
follows : 1 . A resolution - refutation tree is found by some search process . The
unification subsets of the clauses in this tree are marked . 2 . New variables are ...
In conclusion , the steps of the answer extraction process can be summarized as
follows : 1 . A resolution - refutation tree is found by some search process . The
unification subsets of the clauses in this tree are marked . 2 . New variables are ...
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Contents
PRODUCTION SYSTEMS AND AI | 17 |
SEARCH STRATEGIES FOR | 53 |
SEARCH STRATEGIES FOR DECOMPOSABLE | 99 |
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 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 search tree selected sequence shown in Figure simple solution graph solve specify statement step STRIPS structure subgoal substitutions successors Suppose symbols termination theorem tree 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 |