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Artificial Intelligence : Structures and Strategies for Complex Problem Solving (3rd)

ISBN: 9780805311969 | 0805311963
Edition: 3rd
Format: Hardcover
Publisher: Addison-Wesley
Pub. Date: 9/1/1997

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SummaryTable of Contents
This successful book provides a balanced perspective on the languages, schools, theories, and applications of Artificial Intelligence. Now in its third edition, Artificial Intelligence contains an expanded presentation of case based reasoning, genetic algorithms, neural nets, agents, and stochastic models of natural language understanding. In addition, the book contains a discussion of emergent computation and artificial-life.
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Prefacevii
PART I ARTIFICIAL INTELLIGENCE: ITS ROOTS AND SCOPE1(33)
Artificial Intelligence--An Attempted Definition1(2)
1 AI: HISTORY AND APPLICATIONS
3(31)
1.1 From Eden to ENIAC: Attitudes toward Intelligence, Knowledge, and Human Artifice
3(14)
1.1.1 Historical Foundations
4(3)
1.1.2 The Development of Logic
7(3)
1.1.3 The Turing Test
10(3)
1.1.4 Biological and Social Models of Intelligence: Agent-Oriented Problem Solving
13(4)
1.2 Overview of AI Application Areas
17(11)
1.2.1 Game Playing
18(1)
1.2.2 Automated Reasoning and Theorem Proving
19(1)
1.2.3 Expert Systems
20(2)
1.2.4 Natural Language Understanding and Semantic Modeling
22(1)
1.2.5 Modeling Human Performance
23(1)
1.2.6 Planning and Robotics
23(2)
1.2.7 Languages and Environments for AI
25(1)
1.2.8 Machine Learning
25(1)
1.2.9 Parallel Distributed Processing (PDP) and Emergent Computation
26(1)
1.2.10 AI and Philosophy
27(1)
1.3 Artificial Intelligence--A Summary
28(1)
1.4 Epilogue and References
29(1)
1.5 Exercises
30(4)
PART II ARTIFICIAL INTELLIGENCE AS REPRESENTATION AND SEARCH34(173)
Knowledge Representation34(7)
Problem Solving as Search41(6)
2 THE PREDICATE CALCULUS
47(34)
2.0 Introduction
47(1)
2.1 The Propositional Calculus
47(5)
2.1.1 Symbols and Sentences
47(2)
2.1.2 The Semantics of the Propositional Calculus
49(3)
2.2 The Predicate Calculus
52(12)
2.2.1 The Syntax of Predicates and Sentences
52(6)
2.2.2 A Semantics for the Predicate Calculus
58(6)
2.3 Using Inference Rules to Produce Predicate Calculus Expressions
64(11)
2.3.1 Inference Rules
64(4)
2.3.2 Unification
68(4)
2.3.3 A Unification Example
72(3)
2.4 Application: A Logic-Based Financial Advisor
75(4)
2.5 Epilogue and References
79(1)
2.6 Exercises
79(2)
3 STRUCTURES AND STRATEGIES FOR STATE SPACE SEARCH
81(42)
3.0 Introduction
81(3)
3.1 Graph Theory
84(9)
3.1.1 Structures for State Space Search
84(3)
3.1.2 State Space Representation of Problems
87(6)
3.2 Strategies for State Space Search
93(14)
3.2.1 Data-Driven and Goal-Driven Search
93(3)
3.2.2 Implementing Graph Search
96(3)
3.2.3 Depth-First and Breadth-First Search
99(7)
3.2.4 Depth-First Search with Iterative Deepening
106(1)
3.3 Using the State Space to Represent Reasoning with the Predicate Calculus
107(14)
3.3.1 State Space Description of a Logical System
107(2)
3.3.2 And/Or Graphs
109(2)
3.3.3 Further Examples and Applications
111(10)
3.4 Epilogue and References
121(1)
3.5 Exercises
121(2)
4 HEURISTIC SEARCH
123(36)
4.0 Introduction
123(4)
4.1 An Algorithm for Heuristic Search
127(12)
4.1.1 Implementing "Best-First" Search
127(4)
4.1.2 Implementing Heuristic Evaluation Functions
131(5)
4.1.3 Heuristic Search and Expert Systems
136(3)
4.2 Admissibility, Monotonicity, and Informedness
139(5)
4.2.1 Admissibility Measures
139(2)
4.2.2 Monotonicity
141(1)
4.2.3 When One Heuristic Is Better: More Informed Heuristics
142(2)
4.3 Using Heuristics in Games
144(8)
4.3.1 The Minimax Procedure on Exhaustively Searchable Graphs
144(3)
4.3.2 Minimaxing to Fixed Ply Depth
147(3)
4.3.3 The Alpha-Beta Procedure
150(2)
4.4 Complexity Issues
152(4)
4.5 Epilogue and References
156(1)
4.6 Exercises
156(3)
5 CONTROL AND IMPLEMENTATION OF STATE SPACE SEARCH
159(48)
5.0 Introduction
159(1)
5.1 Recursion-Based Search
160(4)
5.1.1 Recursion
160(1)
5.1.2 Recursive Search
161(3)
5.2 Pattern-Directed Search
164(7)
5.2.1 Example: Recursive Search in the Knight's Tour Problem
165(3)
5.2.2 Refining the Pattern-search Algorithm
168(3)
5.3 Production Systems
171(15)
5.3.1 Definition and History
171(3)
5.3.2 Examples of Production Systems
174(6)
5.3.3 Control of Search in Production Systems
180(4)
5.3.4 Advantages of Production Systems for AI
184(2)
5.4 Predicate Calculus and Planning
186(10)
5.5 The Blackboard Architecture for Problem Solving
196(2)
5.6 Epilogue and References
198(1)
5.7 Exercises
199(8)
PART III REPRESENTATIONS FOR KNOWLEDGE-BASED PROBLEM SOLVING207(133)
6 KNOWLEDGE-INTENSIVE PROBLEM SOLVING
207(40)
6.0 Introduction
207(3)
6.1 Overview of Expert System Technology
210(9)
6.1.1 The Design of Rule-Based Expert Systems
210(2)
6.1.2 Selecting a Problem for Expert System Development
212(2)
6.1.3 The Knowledge Engineering Process
214(2)
6.1.4 Conceptual Models and Their Role in Knowledge Acquisition
216(3)
6.2 Rule-based Expert Systems
219(12)
6.2.1 The Production System and Goal-driven Problem Solving
220(4)
6.2.2 Explanation and Transparency in Goal-driven Reasoning
224(2)
6.2.3 Using the Production System for Data-driven Reasoning
226(3)
6.2.4 Heuristics and Control in Expert Systems
229(1)
6.2.5 Conclusions: Rule-Based Reasoning
230(1)
6.3 Model-based Reasoning
231(4)
6.3.1 Introduction
231(4)
6.4 Case-based Reasoning
235(5)
6.4.1 Introduction
235(5)
6.5 The Knowledge-Representation Problem
240(5)
6.6 Epilogue and References
245(1)
6.7 Exercises
246(1)
7 REASONING WITH UNCERTAIN OR INCOMPLETE INFORMATION
247(46)
7.0 Introduction
247(2)
7.1 The Statistical Approach to Uncertainty
249(20)
7.1.1 Bayesian Reasoning
250(4)
7.1.2 Bayesian Belief Networks
254(5)
7.1.3 The Dempster-Shafer Theory of Evidence
259(4)
7.1.4 The Stanford Certainty Factor Algebra
263(3)
7.1.5 Causal Networks
266(3)
7.2 Introduction to Nonmonotonic Systems
269(15)
7.2.1 Logics for Nonmonotonic Reasoning
269(4)
7.2.2 Logics Based on Minimum Models
273(2)
7.2.3 Truth Maintenance Systems
275(6)
7.2.4 Set Cover and Logic Based Abduction (Stern 1996)
281(3)
7.3 Reasoning with Fuzzy Sets
284(5)
7.4 Epilogue and References
289(1)
7.5 Exercises
290(3)
8 KNOWLEDGE REPRESENTATION
293(47)
8.0 Knowledge Representation Languages
293(2)
8.1 Issues in Knowledge Representation
295(2)
8.2 A Survey of Network Representation
297(12)
8.2.1 Associationist Theories of Meaning
297(4)
8.2.2 Early Work in Semantic Nets
301(2)
8.2.3 Standardization of Network Relationships
303(6)
8.3 Conceptual Graphs: A Network Representation Language
309(11)
8.3.1 Introduction to Conceptual Graphs
309(2)
8.3.2 Types, Individuals, and Names
311(2)
8.3.3 The Type Hierarchy
313(1)
8.3.4 Generalization and Specialization
314(3)
8.3.5 Propositional Nodes
317(1)
8.3.6 Conceptual Graphs and Logic
318(2)
8.4 Structured Representations
320(8)
8.4.1 Frames
320(4)
8.4.2 Scripts
324(4)
8.5 Issues in Knowledge Representation
328(6)
8.5.1 Hierarchies, Inheritance, and Exceptions
328(3)
8.5.2 Naturalness, Efficiency, and Plasticity
331(3)
8.6 Epilogue and References
334(1)
8.7 Exercises
335(5)
PART IV LANGUAGES AND PROGRAMMING TECHNIQUES FOR ARTIFICIAL INTELLIGENCE340(177)
Languages, Understanding, and Levels of Abstraction340(2)
Desired Features of AI Language342(7)
An Overview of LISP and PROLOG349(3)
Object-Oriented Programming352(1)
Hybrid Environments353(1)
A Hybrid Example354(2)
Selecting an Implementation Language356(1)
9 AN INTRODUCTION TO PROLOG
357(68)
9.0 Introduction
357(1)
9.1 Syntax for Predicate Calculus Programming
358(13)
9.1.1 Representing Facts and Rules
358(4)
9.1.2 Creating, Changing, and Monitoring the PROLOG Environment
362(2)
9.1.3 Recursion-Based Search in PROLOG
364(2)
9.1.4 Recursive Search in PROLOG
366(3)
9.1.5 The Use of Cut to Control Search in PROLOG
369(2)
9.2 Abstract Data Types (ADTs) in PROLOG
371(4)
9.2.1 The ADT Stack
371(2)
9.2.2 The ADT Queue
373(1)
9.2.3 The ADT Priority Queue
373(1)
9.2.4 The ADT Set
374(1)
9.3 A Production System Example in PROLOG
375(6)
9.4 Designing Alternative Search Strategies
381(5)
9.4.1 Depth-First Search Using the Closed List
381(2)
9.4.2 Breadth-First Search in PROLOG
383(1)
9.4.3 Best-First Search in PROLOG
384(2)
9.5 A PROLOG Planner
386(3)
9.6 PROLOG: Meta-Predicates, Types, and Unification
389(8)
9.6.1 Meta-Logical Predicates
389(2)
9.6.2 Types in PROLOG
391(3)
9.6.3 Unification, the Engine for Predicate Matching and Evaluation
394(3)
9.7 Meta-Interpreters in PROLOG
397(18)
9.7.1 An Introduction to Meta-Interpreters: PROLOG in PROLOG
397(4)
9.7.2 Shell for a Rule-Based Expert System
401(9)
9.7.3 Semantic Nets in PROLOG
410(2)
9.7.4 Frames and Schemata in PROLOG
412(3)
9.8 PROLOG: Towards Nonprocedural Computing
415(6)
9.9 Epilogue and References
421(1)
9.10 Exercises
422(3)
10 AN INTRODUCTION TO LISP
425(92)
10.0 Introduction
425(1)
10.1 LISP: A Brief Overview
426(23)
10.1.1 Symbolic Expressions, the Syntactic Basis of LISP
426(4)
10.1.2 Control of LISP Evaluation: quote and eval
430(1)
10.1.3 Programming in LISP: Creating New Functions
431(2)
10.1.4 Program Control in LISP: Conditionals and Predicates
433(3)
10.1.5 Functions, Lists, and Symbolic Computing
436(2)
10.1.6 Lists as Recursive Structures
438(3)
10.1.7 Nested Lists, Structure, and car/cdr Recursion
441(3)
10.1.8 Binding Variables Using Set
444(2)
10.1.9 Defining Local Variables Using let
446(2)
10.1.10 Data Types in Common LISP
448(1)
10.1.11 Conclusion
449(1)
10.2 Search in LISP: A Functional Approach to the Farmer, Wolf, Goat, and Cabbage Problem
449(6)
10.3 Higher-Order Functions and Procedural Abstraction
455(4)
10.3.1 Maps and Filters
455(2)
10.3.2 Functional Arguments and Lambda Expressions
457(2)
10.4 Search Strategies in LISP
459(4)
10.4.1 Breadth-First and Depth-First Search
459(3)
10.4.2 Best-First Search
462(1)
10.5 Pattern Matching in LISP
463(2)
10.6 A Recursive Unification Function
465(4)
10.6.1 Implementing the Unification Algorithm
465(2)
10.6.2 Implementing Substitution Sets Using Association Lists
467(2)
10.7 Interpreters and Embedded Languages
469(3)
10.8 Logic Programming in LISP
472(10)
10.8.1 A Simple Logic Programming Language
472(2)
10.8.2 Streams and Stream Processing
474(3)
10.8.3 A Stream-Based Logic Programming Interpreter
477(5)
10.9 Streams and Delayed Evaluation
482(4)
10.10 An Expert System Shell in LISP
486(8)
10.10.1 Implementing Certainty Factors
486(2)
10.10.2 Architecture of lisp-shell
488(2)
10.10.3 User Queries and Working Memory
490(1)
10.10.4 Classification Using lisp-shell
491(3)
10.11 Network Representations and Inheritance
494(3)
10.11.1 Representing Semantic Nets in LISP
494(3)
10.11.2 Implementing Inheritance
497(1)
10.12 Object-Oriented Programming Using CLOS
497(14)
10.12.1 Defining Classes and Instances in CLOS
499(2)
10.12.2 Defining Generic Functions and Methods
501(2)
10.12.3 Inheritance in CLOS
503(2)
10.12.4 Advanced Features of CLOS
505(1)
10.12.5 Example: A Thermostat Simulation
505(6)
10.13 Epilogue and References
511(1)
10.14 Exercises
511(6)
PART V ADVANCED TOPICS FOR AI PROBLEM SOLVING517(234)
Natural Language, Automated Reasoning, and Learning517(2)
11 UNDERSTANDING NATURAL LANGUAGE
519(40)
11.0 Role of Knowledge in Language Understanding
519(3)
11.1 Language Understanding: A Symbolic Approach
522(2)
11.1.1 Introduction
522(1)
11.1.2 Stages of Language Analysis
523(1)
11.2 Syntax
524(10)
11.2.1 Specification and Parsing Using Context-Free Grammars
524(3)
11.2.2 Transition Network Parsers
527(4)
11.2.3 The Chomsky Hierarchy and Context-Sensitive Grammars
531(3)
11.3 Combining Syntax and Semantics in ATN Parsers
534(9)
11.3.1 Augmented Transition Network Parsers
534(4)
11.3.2 Combining Syntax and Semantics
538(5)
11.4 Stochastic Tools for Language Analysis
543(7)
11.4.1 Introduction
543(2)
11.4.2 A Markov Model Approach
545(1)
11.4.3 A CART Tree Approach
546(1)
11.4.4 Mutual Information Clustering
547(1)
11.4.5 Parsing
548(2)
11.4.6 Other Language Applications for Stochastic Techniques
550(1)
11.5 Natural Language Applications
550(5)
11.5.1 Story Understanding and Question Answering
550(1)
11.5.2 A Database Front End
551(4)
11.6 Epilogue and References
555(2)
11.7 Exercises
557(2)
12 AUTOMATED REASONING
559(44)
12.0 Introduction to Weak Methods in Theorem Proving
559(1)
12.1 The General Problem Solver and Difference Tables
560(6)
12.2 Resolution Theorem Proving
566(21)
12.2.1 Introduction
566(2)
12.2.2 Producing the Clause Form for Resolution Refutations
568(5)
12.2.3 The Binary Resolution Proof Procedure
573(5)
12.2.4 Strategies and Simplification Techniques for Resolution
578(5)
12.2.5 Answer Extraction from Resolution Refutations
583(4)
12.3 PROLOG and Automated Reasoning
587(6)
12.3.1 Introduction
587(1)
12.3.2 Logic Programming and PROLOG
588(5)
12.4 Further Issues in Automated Reasoning
593(7)
12.4.1 Uniform Representations for Weak Method Solutions
593(4)
12.4.2 Alternative Inference Rules
597(2)
12.4.3 Search Strategies and Their Use
599(1)
12.5 Epilogue and References
600(1)
12.6 Exercises
601(2)
13 MACHINE LEARNING: SYMBOL-BASED
603(58)
13.0 Introduction
603(3)
13.1 A Framework for Symbol-based Learning
606(6)
13.2 Version Space Search
612(12)
13.2.1 Generalization Operators and the Concept Space
612(1)
13.2.2 The Candidate Elimination Algorithm
613(7)
13.2.3 LEX: Inducing Search Heuristics
620(3)
13.2.4 Evaluating Candidate Elimination
623(1)
13.3 The ID3 Decision Tree Induction Algorithm
624(9)
13.3.1 Top-Down Decision Tree Induction
627(1)
13.3.2 Information Theoretic Test Selection
628(4)
13.3.3 Evaluating ID3
632(1)
13.3.4 Decision Tree Data Issues: Bagging, Boosting
632(1)
13.4 Inductive Bias and Learnability
633(5)
13.4.1 Inductive Bias
634(2)
13.4.2 The Theory of Learnability
636(2)
13.5 Knowledge and Learning
638(11)
13.5.1 Meta-DENDRAL
639(1)
13.5.2 Explanation-Based Learning
640(5)
13.5.3 EBL and Knowledge-Level Learning
645(1)
13.5.4 Analogical Reasoning
646(3)
13.6 Unsupervised Learning
649(9)
13.6.1 Discovery and Unsupervised Learning
649(2)
13.6.2 Conceptual Clustering
651(2)
13.6.3 COBWEB and the Structure of Taxonomic Knowledge
653(5)
13.7 Epilogue and References
658(1)
13.8 Exercises
659(2)
14 MACHINE LEARNING: CONNECTIONIST
661(52)
14.0 Introduction
661(2)
14.1 Foundations for Connectionist Networks
663(3)
14.1.1 Early History
663(3)
14.2 Perceptron Learning
666(9)
14.2.1 The Perceptron Training Algorithm
666(2)
14.2.2 An Example: Using a Perceptron Network to Classify
668(4)
14.2.3 The Delta Rule
672(3)
14.3 Backpropagation Learning
675(7)
14.3.1 Deriving the Backpropagation Algorithm
675(4)
14.3.2 Backpropagation Example 1: NETtalk
679(2)
14.3.3 Backpropagation Example 2: Exclusive-or
681(1)
14.4 Competitive Learning
682(8)
14.4.1 Winner-Take-All Learning for Classification
682(2)
14.4.2 A Kohonen Network for Learning Prototypes
684(2)
14.4.3 Grossberg Learning and Counterpropagation
686(4)
14.5 Hebbian Coincidence Learning
690(11)
14.5.1 Introduction
690(1)
14.5.2 An Example of Unsupervised Hebbian Learning
691(3)
14.5.3 Supervised Hebbian Learning
694(2)
14.5.4 Associative Memory and the Linear Associator
696(5)
14.6 Attractor Networks or "Memories"
701(10)
14.6.1 Introduction
701(1)
14.6.2 BAM, the Bi-directional Associative Memory
702(2)
14.6.3 Examples of BAM Processing
704(2)
14.6.4 Autoassociative Memory and Hopfield Nets
706(5)
14.7 Epilogue and References
711(1)
14.8 Exercises
712(1)
15 MACHINE LEARNING: SOCIAL AND EMERGENT
713(38)
15.0 Social and Emergent Models of Learning
713(2)
15.1 The Genetic Algorithm
715(10)
15.1.3 Two Examples: CNF Satisfaction and the Traveling Salesperson
717(4)
15.1.4 Evaluating the Genetic Algorithm
721(4)
15.2 Classifier Systems and Genetic Programming
725(11)
15.2.1 Classifier Systems
725(5)
15.2.2 Programming with Genetic Operators
730(6)
15.3 Artificial Life and Society-based Learning
736(11)
15.3.1 The "Game of Life"
737(3)
15.3.2 Evolutionary Programming
740(3)
15.3.3 A Case Study in Emergence (Crutchfield and Mitchell 1994)
743(4)
15.4 Epilogue and References
747(1)
15.5 Exercises
748(3)
PART VI EPILOGUE751(30)
Reflections on the Nature of Intelligence751(2)
16 ARTIFICIAL INTELLIGENCE AS EMPIRICAL ENQUIRY
753(28)
16.0 Introduction
753(2)
16.1 Artificial Intelligence: A Revised Definition
755(11)
16.1.1 Intelligence and the Physical Symbol System
756(3)
16.1.2 Minds, Brains, and Neural Computing
759(2)
16.1.3 Agents, Emergence, and Intelligence
761(3)
16.1.4 Situated Actors and the Existential Mind
764(2)
16.2 Cognitive Science: An Overview
766(4)
16.2.1 The Analysis of Human Performance
766(1)
16.2.2 The Production System and Human Cognition
767(3)
16.3 Current Issues in Machine Learning
770(5)
16.4 Understanding Intelligence: Issues and Directions
775(5)
16.5 Epilogue and References
780(1)
Bibliography781(22)
Author Index803(6)
Subject Index809(14)
Acknowledgements823

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