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 23
... function . 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 ...
... function . 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 ...
Page 73
... 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 ; distance or difference metrics ...
... 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 ; distance or difference metrics ...
Page 74
... function has resulted in substantially fewer nodes being expanded . ( If we simply use the evaluation function f ( n ) = d ( n ) , we get the breadth - first search process . ) The choice of evaluation function critically determines ...
... function has resulted in substantially fewer nodes being expanded . ( If we simply use the evaluation function f ( n ) = d ( n ) , we get the breadth - first search process . ) The choice of evaluation function critically determines ...
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 |