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 72
HEURISTIC GRAPH - SEARCH PROCEDURES The uninformed search methods , whether breadth - first or depth - first , are exhaustive methods for finding paths to a goal node . In principle , these methods provide a solution to the path ...
HEURISTIC GRAPH - SEARCH PROCEDURES The uninformed search methods , whether breadth - first or depth - first , are exhaustive methods for finding paths to a goal node . In principle , these methods provide a solution to the path ...
Page 85
THE HEURISTIC POWER OF EVALUATION FUNCTIONS The selection of the heuristic function is crucial in determining the heuristic power of search algorithm A. Using h = 0 assures admissibility but results in a breadth - first search and is ...
THE HEURISTIC POWER OF EVALUATION FUNCTIONS The selection of the heuristic function is crucial in determining the heuristic power of search algorithm A. Using h = 0 assures admissibility but results in a breadth - first search and is ...
Page 103
AO * : A HEURISTIC SEARCH PROCEDURE FOR AND / OR GRAPHS As with ordinary graphs , we define the process of expanding a node as the application of a successor operator that generates all of the successors of a node ( through all outgoing ...
AO * : A HEURISTIC SEARCH PROCEDURE FOR AND / OR GRAPHS As with ordinary graphs , we define the process of expanding a node as the application of a successor operator that generates all of the successors of a node ( through all outgoing ...
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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
8-puzzle achieve actions Adders AI production algorithm AND/OR graph applied Artificial Intelligence atomic formula backed-up value backtracking backward block breadth-first breadth-first search called chapter clause form CLEAR(C component contains control regime control strategy cost DCOMP Deleters delineation depth-first search described discussed disjunction domain element-of evaluation function example existentially quantified F-rule formula frame problem global database goal expression goal node goal stack goal wff graph-search HANDEMPTY heuristic HOLDING(A implication initial state description knowledge leaf nodes literal nodes monotone restriction negation node labeled ONTABLE(A optimal path pickup(A precondition predicate calculus procedure production system prove recursive regress represent representation resolution refutation result robot problem rule applications rule-based deduction systems search graph search tree semantic network sequence shown in Figure Skolem function solution graph solve stack(A STRIPS structure subgoal substitutions successors Suppose symbols termination condition theorem theorem-proving tip nodes unifying composition universally quantified unstack(C,A WORKS-IN
Bibliographic information
| Title | Principles of Artificial Intelligence |
| Author | Nils J. Nilsson |
| Edition | illustrated, reprint |
| Publisher | Morgan Kaufmann Publishers, 1986 |
| Original from | the University of Virginia |
| Digitized | 27 Jul 2009 |
| ISBN | 0934613109, 9780934613101 |
| Length | 476 pages |
| Export Citation | BiBTeX EndNote RefMan |