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 86
Page 197
For example , the fact expression : ( Bu ) ( Vv ) { Q ( vu ) ^ ~ [ [ R ( v ) V P ( v ) ] ^ S
( u , v ) ] } is converted to Q ( v , A ) ^ { [ ~ R ( v ) ~ ~ P ( v ) ] V ~ S ( A , v ) ] } .
Variables can be renamed so that the same variable does not occur in different (
main ) ...
For example , the fact expression : ( Bu ) ( Vv ) { Q ( vu ) ^ ~ [ [ R ( v ) V P ( v ) ] ^ S
( u , v ) ] } is converted to Q ( v , A ) ^ { [ ~ R ( v ) ~ ~ P ( v ) ] V ~ S ( A , v ) ] } .
Variables can be renamed so that the same variable does not occur in different (
main ) ...
Page 215
Our explanation of the appropriateness of this operation is dual to the
explanation for applying an F - rule to a fact AND / OR graph . The assertional
rule W L can be negated and added ( disjunctively ) to the goal wff . The negated
form is ( W 1 ...
Our explanation of the appropriateness of this operation is dual to the
explanation for applying an F - rule to a fact AND / OR graph . The assertional
rule W L can be negated and added ( disjunctively ) to the goal wff . The negated
form is ( W 1 ...
Page 254
These structures can be joined by match edges at nodes labeled by literals that
unify . We label the match edges themselves by the corresponding mgus . In the
initial graphs , match edges between the fact and goal graphs must be between ...
These structures can be joined by match edges at nodes labeled by literals that
unify . We label the match edges themselves by the corresponding mgus . In the
initial graphs , match edges between the fact and goal graphs must be between ...
What people are saying - Write a review
We haven't found any reviews in the usual places.
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 |