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 83
Page 23
Our hill - climbing function must be such that it attains its highest value for a
database satisfying the termination condition . Applying hill - climbing to the 8 -
puzzle we might use , as a function of the state description , the negative of the
number ...
Our hill - climbing function must be such that it attains its highest value for a
database satisfying the termination condition . Applying hill - climbing to the 8 -
puzzle we might use , as a function of the state description , the negative of the
number ...
Page 73
One important method uses a real - valued function over the nodes called an
evaluation function . Evaluation functions have been based on a variety of ideas :
Attempts have been made to define the probability that a node is on the best path
...
One important method uses a real - valued function over the nodes called an
evaluation function . Evaluation functions have been based on a variety of ideas :
Attempts have been made to define the probability that a node is on the best path
...
Page 74
2.8 A search tree using an evaluation function . expanded . We see that the same
solution path is found here as was found by the other search methods , although
the use of the evaluation function has resulted in substantially fewer nodes ...
2.8 A search tree using an evaluation function . expanded . We see that the same
solution path is found here as was found by the other search methods , although
the use of the evaluation function has resulted in substantially fewer nodes ...
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 evaluation example 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 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 |