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 66
node ( except the root node ) of a tree is the successor of only one node and thus is generated once only when its unique ... In this case , each member of M is added to OPEN and is installed in the search tree as a successor of n .
node ( except the root node ) of a tree is the successor of only one node and thus is generated once only when its unique ... In this case , each member of M is added to OPEN and is installed in the search tree as a successor of n .
Page 67
graph and search tree shown in Figure 2.4 . The dark arrows along certain arcs in this search graph are the pointers that define parents of nodes in the search tree . The solid nodes are on CLOSED , and the other nodes are on OPEN at ...
graph and search tree shown in Figure 2.4 . The dark arrows along certain arcs in this search graph are the pointers that define parents of nodes in the search tree . The solid nodes are on CLOSED , and the other nodes are on OPEN at ...
Page 80
It is reasonable to say that A * with h ( n ) = W ( n ) is more informed than breadth - first search , which uses h ( n ) = 0 . ... We prove this result using induction on the depth of a node in the A , search tree at termination .
It is reasonable to say that A * with h ( n ) = W ( n ) is more informed than breadth - first search , which uses h ( n ) = 0 . ... We prove this result using induction on the depth of a node in the A , search tree at termination .
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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 |