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Artificial Intelligence : Structures and Strategies for Complex Problem Solving,9780321263186
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Artificial Intelligence : Structures and Strategies for Complex Problem Solving

by
Edition:
6th
ISBN13:

9780321263186

ISBN10:
0321263189
Format:
Hardcover
Pub. Date:
1/1/2009
Publisher(s):
Addison Wesley

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What version or edition is this?
This is the 6th edition with a publication date of 1/1/2009.
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Summary

[Shelving Category: Artificial Intelligence] "One of the few books on the market that covers all the topics I have included in my course for the past 10 years." Bruce Maxim, University of Michigan Dearborn "The book is a perfect complement to an AI course. It gives the reader both an historical point of view and a practical guide to all the techniques. It is THE book I recommEND as an introduction to this field." Pascal RebreyEND, Dalarna University "Excellent additions and improvements. I will use the 5th EDITION in my introduction and advanced AI courses." Peter Funk, Mtrade;lardalen University "The style of writing and comprehensive treatment of the subject matter makes this a valuable addition to the AI literature." Malachy Eaton, University of Limerick Can machines think like people? This question is the driving force behind Artificial Intelligence, but it is only the starting point of this ever-evolving, exciting discipline. AI uses different strategies to solve the complex problems that arise wherever computer technology is applied, from those areas pertaining to perception and adaptation (neural networks, genetic algorithms) to the fields of intelligent agents, natural language understanding and sTOChastic models. George Luger examines complex problem solving techniques while demonstrating his enthusiasm and excitement for the study of intelligence itself. He shows how to use a number of different software tools and techniques to address the many challenges faced by today's computer scientists. New to this EDITION Brand new chapter which introduces the sTOChastic methodology. ExtENDed material in many sections addresses the continuing importance of agent-based problem solving and embodiment in AI technology. Presentation of issues in natural language understanding, including sections on sTOChastic methods for language comprehension; Markov models; CART trees; mutual information clustering; and statistic based parsing. Further discussion of the AI ENDeavor from the perspectives of philosophy, psychology, and neuro-psychology. Artificial Intelligence: Structures and Strategies for Complex Problem Solving is ideal for a one or two semester university course on AI, as well as an invaluable reference for researchers in the field or practitioners wishing to employ the power of current AI techniques in their work. After receiving his PhD from the University of Pennsylvania,George Lugerspent five years researching and teaching at the Department of Artificial Intelligence of the University of Edinburgh. He is currently a Professor of Computer Science, Linguistics, and Psychology at the University of New Mexico.

Author Biography

George Luger is currently a Professor of Computer Science, Linguistics, and Psychology at the University of New Mexico.

Table of Contents

Preface vii
Publisher's Acknowledgements xvi
PART I ARTIFICIAL INTELLIGENCE: ITS ROOTS AND SCOPE
1(34)
AI: Early History and Applications
3(32)
From Eden to ENIAC: Attitudes toward Intelligence, Knowledge, and Human Artifice
3(17)
Overview of AI Application Areas
20(10)
Artificial Intelligence---A Summary
30(1)
Epilogue and References
31(2)
Exercises
33(2)
PART II ARTIFICIAL INTELLIGENCE AS REPRESENTATION AND SEARCH
35(188)
The Predicate Calculus
45(34)
Introduction
45(1)
The Propositional Calculus
45(5)
The Predicate Calculus
50(12)
Using Inference Rules to Produce Predicate Calculus Expressions
62(11)
Application: A Logic-Based Financial Advisor
73(4)
Epilogue and References
77(1)
Exercises
77(2)
Structures and Strategies for State Space Search
79(44)
Introduction
79(3)
Graph Theory
82(11)
Strategies for State Space Search
93(14)
Using the State Space to Represent Reasoning with the Predicate Calculus
107(14)
Epilogue and References
121(1)
Exercises
121(2)
Heuristic Search
123(42)
Introduction
123(4)
Hill-Climbing and Dynamic Programming
127(6)
The Best-First Search Algorithm
133(12)
Admissibility, Monotonicity, and Informedness
145(5)
Using Heuristics in Games
150(7)
Complexity Issues
157(4)
Epilogue and References
161(1)
Exercises
162(3)
Stochastic Methods
165(28)
Introduction
165(2)
The Elements of Counting
167(3)
Elements of Probability Theory
170(12)
Applications of the Stochastic Methodology
182(2)
Bayes' Theorem
184(6)
Epilogue and References
190(1)
Exercises
191(2)
Building Control Algorithms for State Space Search
193(30)
Introduction
193(1)
Recursion-Based Search
194(6)
Production Systems
200(17)
The Blackboard Architecture for Problem Solving
217(2)
Epilogue and References
219(1)
Exercises
220(3)
PART III REPRESENTATION AND INTELLIGENCE: THE AI CHALLENGE
223(162)
Knowledge Representation
227(50)
Issues in Knowledge Representation
227(1)
A Brief History of AI Representational Schemes
228(20)
Conceptual Graphs: A Network Language
248(10)
Alternatives to Explicit Representation
258(7)
Agent-Based and Distributed Problem Solving
265(5)
Epilogue and References
270(3)
Exercises
273(4)
Strong Method Problem Solving
277(56)
Introduction
277(2)
Overview of Expert System Technology
279(7)
Rule-Based Expert Systems
286(12)
Model-Based, Case-Based, and Hybrid Systems
298(16)
Planning
314(15)
Epilogue and References
329(2)
Exercises
331(2)
Reasoning in Uncertain Situations
333(52)
Introduction
333(2)
Logic-Based Abductive Inference
335(15)
Abduction: Alternatives to Logic
350(13)
The Stochastic Approach to Uncertainty
363(16)
Epilogue and References
379(2)
Exercises
381(4)
PART IV MACHINE LEARNING
385(160)
Machine Learning: Symbol-Based
387(66)
Introduction
387(3)
A Framework for Symbol-based Learning
390(6)
Version Space Search
396(12)
The ID3 Decision Tree Induction Algorithm
408(9)
Inductive Bias and Learnability
417(5)
Knowledge and Learning
422(11)
Unsupervised Learning
433(9)
Reinforcement Learning
442(7)
Epilogue and References
449(1)
Exercises
450(3)
Machine Learning: Connectionist
453(54)
Introduction
453(2)
Foundations for Connectionist Networks
455(3)
Perceptron Learning
458(9)
Backpropagation Learning
467(7)
Competitive Learning
474(10)
Hebbian Coincidence Learning
484(11)
Attractor Networks or ``Memories''
495(10)
Epilogue and References
505(1)
Exercises
506(1)
Machine Learning: Social and Emergent
507(38)
Social and Emergent Models of Learning
507(2)
The Genetic Algorithm
509(10)
Classifier Systems and Genetic Programming
519(11)
Artificial Life and Society-Based Learning
530(11)
Epilogue and References
541(1)
Exercises
542(3)
PART V ADVANCED TOPICS FOR AI PROBLEM SOLVING
545(90)
Automated Reasoning
547(44)
Introduction to Weak Methods in Theorem Proving
547(1)
The General Problem Solver and Difference Tables
548(6)
Resolution Theorem Proving
554(21)
Prolog and Automated Reasoning
575(6)
Further Issues in Automated Reasoning
581(7)
Epilogue and References
588(1)
Exercises
589(2)
Understanding Natural Language
591(44)
The Natural Language Understanding Problem
591(3)
Deconstructing Language: A Symbolic Analysis
594(3)
Syntax
597(9)
Syntax and Knowledge with ATN Parsers
606(10)
Stochastic Tools for Language Analysis
616(7)
Natural Language Applications
623(7)
Epilogue and References
630(2)
Exercises
632(3)
PART VI LANGUAGES AND PROGRAMMING TECHNIQUES FOR ARTIFICIAL INTELLIGENCE
635(186)
An Introduction to Prolog
641(82)
Introduction
641(1)
Syntax for Predicate Calculus Programming
642(12)
Abstract Data Types (ADTs) in Prolog
654(4)
A Production System Example in Prolog
658(5)
Designing Alternative Search Strategies
663(5)
A Prolog Planner
668(3)
Prolog: Meta-Predicates, Types, and Unification
671(8)
Meta-Interpreters in Prolog
679(15)
Learning Algorithms in Prolog
694(10)
Natural Language Processing in Prolog
704(12)
Epilogue and References
716(3)
Exercises
719(4)
An Introduction to Lisp
723(98)
Introduction
723(1)
LISP: A Brief Overview
724(22)
Search in LISP: A Functional Approach to the Farmer, Wolf, Goat, and Cabbage Problem
746(5)
Higher-Order Functions and Abstraction
751(4)
Search Strategies in LISP
755(4)
Pattern Matching in LISP
759(2)
A Recursive Unification Function
761(4)
Interpreters and Embedded Languages
765(2)
Logic Programming in LISP
767(9)
Streams and Delayed Evaluation
776(4)
An Expert System Shell in LISP
780(7)
Semantic Networks and Inheritance in LISP
787(4)
Object-Oriented Programming Using CLOS
791(12)
Learning in LISP: The ID3 Algorithm
803(12)
Epilogue and References
815(1)
Exercises
816(5)
PART VII EPILOGUE
821(34)
Artificial Intelligence as Empirical Enquiry
823(32)
Introduction
823(2)
Artificial Intelligence: A Revised Definition
825(13)
The Science of Intelligent Systems
838(10)
AI: Current Challenges and Future Directions
848(5)
Epilogue and References
853(2)
Bibliography 855(28)
Author Index 883(8)
Subject Index 891


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