Principles of Artificial Intelligence

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Morgan Kaufmann, Jun 28, 2014 - Computers - 476 pages
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A 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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Results 1-5 of 7

Page 69
If the result is saved, then the goal test in step 5 need only look up the result
instead of repeating a possibly costly computation.) The depth-first procedure
generates new databases in an order similar to that generated by an uninformed
 ...
Page 74
The use of an evaluation function that fails to recognize the true promise of some
nodes can result in nonminimal ... the promise of all nodes (such as the
evaluation function yielding breadth-first search) results in expansion of too many
nodes.
Page 78
Thus, we have: RESULT 2: At any time before A* terminates, there exists on
OPEN a node n' that is on an optimal path from s to a goal node, with f(n) < f*(s).
Combining this result with our previous argument, that even the smallest f values
of ...
Page 79
Now A* selected n before termination, so at this time (by RESULT 2) we know
that there existed on OPEN some node n' on an optimal path from s to a goal with
f(n) = f*(s). If n = n', our result is established. Otherwise, we know that A* chose to
 ...
Page 84
f(n2) = g(n2) + h (ng) = g”(n2) + h(n,) (RESULT 7) g*(n1) + c (n1, m2) + h (n2) – g(
n1) + c (n1, n.2) + h (ng) (RESULT 7) Since the monotone restriction implies c(n1,
m2) + h (n2) > h (n1), we have f(n2) > g(n1) + h (n1) = f(n1). Since this fact is ...

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Contents

PROLOGUE
1
CHAPTER 1 PRODUCTION SYSTEMS AND AI
17
CHAPTER 2 SEARCH STRATEGIES FOR AI PRODUCTION SYSTEMS
53
CHAPTER 3 SEARCH STRATEGIES FOR DECOMPOSABLE PRODUCTION SYSTEMS
99
CHAPTER 4 THE PREDICATE CALCULUS IN AI
131
CHAPTER 5 RESOLUTION REFUTATION SYSTEMS
161
CHAPTER 6 RULEBASED DEDUCTION SYSTEMS
193
CHAPTER 7 BASIC PLANGENERATING SYSTEMS
275
CHAPTER 8 ADVANCED PLANGENERATING SYSTEMS
321
CHAPTER 9 STRUCTURED OBJECT REPRESENTATIONS
361
PROSPECTUS
417
BIBLIOGRAPHY
429
AUTHOR INDEX
467
SUBJECT INDEX
471
Copyright

Other editions - View all

Principles of Artificial Intelligence
Nils J. Nilsson
Limited preview - 1982
Principles of Artificial Intelligence
Nils J. Nilsson
Snippet view - 1980
Principles of Artificial Intelligence
Nils J. Nilsson
Snippet view - 1986
View all »

Common terms and phrases

8-puzzle achieve actions algorithm AND/OR graph applied Artificial Intelligence atomic formula backed-up value backtracking backward breadth-first breadth-first search called chapter clause form CLEAR(B component CONT(X contains control regime control strategy cost 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 wif graph-search HANDEMPTY heuristic HOLDING(A implication initial state description knowledge literal nodes logic monotone restriction natural language processing negation node labeled ONTABLE ONTABLE(A optimal path pickup(A precondition predicate calculus problem-solving procedure production rules production system proof prove recursive regress represent representation resolution refutation result robot problem rule applications search graph search tree selected 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 universally quantified unstack(C.A. variables WORKS-IN

About the author (2014)

Nils J. Nilsson's long and rich research career has contributed much to AI. He has written many books, including the classic Principles of Artificial Intelligence. Dr. Nilsson is Kumagai Professor of Engineering, Emeritus, at Stanford University. He has served on the editorial boards of Artificial Intelligence and Machine Learning and as an Area Editor for the Journal of the Association for Computing Machinery. Former Chairman of the Department of Computer Science at Stanford, and former Director of the SRI Artificial Intelligence Center, he is also a past president and Fellow of the American Association for Artificial Intelligence.

Bibliographic information

QR code for Principles of Artificial Intelligence
TitlePrinciples of Artificial Intelligence
AuthorNils J. Nilsson
Editionreprint
PublisherMorgan Kaufmann, 2014
ISBN1483295869, 9781483295862
Length476 pages
Subjects › 

Computers / Data Processing
Computers / Expert Systems
Computers / General
Computers / Intelligence (AI) & Semantics
  
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