Table of contents for Artificial intelligence : structures and strategies for complex problem solving / George F. Luger.


Bibliographic record and links to related information available from the Library of Congress catalog
Note: Electronic data is machine generated. May be incomplete or contain other coding.


Counter
PART I
ARTIFICIAL INTELLIGENCE: ITS ROOTS
AND SCOPE 1
1       Al: HISTORY AND APPLICATIONS        3
1.1     From Eden to ENIAC: Attitudes toward Intelligence, Knowledge, and
Human Artifice 3
1.2     Overview of AI Application Areas 20
1.3     Artificial Intelligence-A Summary  30
1.4     Epilogue and References  31
1.5     Exercises  33
PART II
ARTIFICIAL INTELLIGENCE AS
REPRESENTATION AND SEARCH 35
2       THE PREDICATE CALCULUS          45
2.0     Introduction  45
2.1     The Propositional Calculus 45
2.2     The Predicate Calculus  50
2.3     Using Inference Rules to Produce Predicate Calculus Expressions  62
2.4     Application: A Logic-Based Financial Advisor 73
2.5     Epilogue and References  77
2.6     Exercises  77
3       STRUCTURES AND STRATEGIES FOR STATE SPACE SEARCH                79
3.0    Introduction  79
3.1     Graph Theory  82
3.2     Strategies for State Space Search  93
3.3     Using the State Space to Represent Reasoning with the Predicate Calculus  107
3.4     Epilogue and References  121
3.5     Exercises  121
4       HEURISTIC SEARCH       123
4.0    Introduction  123
4.1     Hill Climbing and Dynamic Programming  127
4.2     The Best-First Search Algorithm  133
4.3     Admissibility, Monotonicity, and Informedness  145
4.4     Using Heuristics in Games  150
4.5     Complexity Issues  157
4.6     Epilogue and References  161
4.7     Exercises  162
5       STOCHASTIC METHODS         165
5.0    Introduction  165
5.1     The Elements of Counting  167
5.2     Elements of Probability Theory  170
5.3     Applications of the Stochastic Methodology  182
5.4     Bayes' Theorem  184
5.5     Epilogue and References  190
5.6     Exercises  191
6       CONTROL AND IMPLEMENTATION OF STATE SPACE SEARCH                193
6.0    Introduction  193
6.1     Recursion-Based Search  194
6.2     Production Systems 200
6.3     The Blackboard Architecture for Problem Solving  187
6.4     Epilogue and References 219
6.5     Exercises 220
PART III
CAPTURING INTELLIGENCE:
THE Al CHALLENGE 223
7       KNOWLEDGE REPRESENTATION           227
7.0    Issues in Knowledge Representation  227
7.1     A Brief History of Al Representational Systems 228
7.2    Conceptual Graphs: A Network Language 248
7.3    Alternative Representations and Ontologies 258
7.4     Agent Based and Distributed Problem Solving  265
7.5    Epilogue and References 270
7.6     Exercises 273
8       STRONG METHOD PROBLEM SOLVING           277
8.0    Introduction  277
8.1    Overview of Expert System Technology  279
8.2    Rule-Based Expert Systems 286
8.3    Model-Based, Case Based, and Hybrid Systems 298
8.4     Planning 314
8.5    Epilogue and References 329
8.6    Exercises 331
9       REASONING IN UNCERTAIN SITUATIONS         333
9.0    Introduction  333
9.1    Logic-Based Abductive Inference 335
9.2     Abduction: Alternatives to Logic 350
9.3    The Stochastic Approach to Uncertainty  363
9.4    Epilogue and References 378
9.5     Exercises 380
PART IV
MACHINE LEARNING 385
10     MACHINE LEARNING: SYMBOL-BASED          387
10.0   Introduction  387
10.1   A Framework for Symbol-based Learning  390
10.2   Version Space Search  396
10.3   The ID3 Decision Tree Induction Algorithm  408
10.4   Inductive Bias and Learnability  417
10.5   Knowledge and Learning  422
10.6   Unsupervised Learning  433
10.7   Reinforcement Learning 442
10.8   Epilogue and References 449
10.9   Exercises 450
11     MACHINE LEARNING: CONNECTIONIST          453
11.0   Introduction  453
11.1   Foundations for Connectionist Networks 455
11.2   Perceptron Learning  458
11.3   Backpropagation Learning  467
11.4   Competitive Learning  474
11.5   Hebbian Coincidence Learning  484
11.6   Attractor Networks or "Memories"  495
11.7   Epilogue and References 505
11.8   Exercises 506
12     MACHINE LEARNING: GENETIC AND EMERGENT              507
12.0   Genetic and Emergent Models of Learning  507
12.1   The Genetic Algorithm  509
12.2   Classifier Systems and Genetic Programming  519
12.3   Artificial Life and Society-Based Learning  530
12.4   Epilogue and References 541
12.5   Exercises 542
13     MACHINE LEARNING: PROBABILISTIC          543
13.0   Stochastic and Dynamic Models of Learning  543
13.1   Hidden Markov Models (HMMs) 544
13.2   Dynamic Bayesian Networks and Learning  554
13.3   Stochastic Extensions to Reinforcement Learning  564
13.4   Epilogue and References 568
13.5   Exercises 570
PART V
ADVANCED TOPICS FOR Al PROBLEM SOLVING 573
14     AUTOMATED REASONING          575
14.0   Introduction to Weak Methods in Theorem Proving  575
14.1   The General Problem Solver and Difference Tables 576
14.2   Resolution Theorem Proving  582
14.3   PROLOG and Automated Reasoning    603
14.4   Further Issues in Automated Reasoning  609
14.5   Epilogue and References 666
14.6   Exercises 667
15     UNDERSTANDING NATURAL LANGUAGE             619
15.0   The Natural Language Understanding Problem  619
15.1   Deconstructing Language: An Analysis 622
15.2   Syntax  625
15.3   Transition Network Parsers and Semantics 633
15.4   Stochastic Tools for Language Understanding  649
15.5   Natural Language Applications 658
15.6   Epilogue and References 630
15.7   Exercises 632
PART VI
EPILOGUE 671
16     ARTIFICIAL INTELLIGENCE AS EMPIRICAL ENQUIRY         673
16.0   Introduction  673
16.1   Artificial Intelligence: A Revised Definition  675
16.2   The Science of Intelligent Systems 688
16.3   AI: Current Challanges and Future Direstions 698
16.4   Epilogue and References 703
Bibliography 705



Library of Congress subject headings for this publication: Artificial intelligence, Knowledge representation (Information theory)Problem solving, Prolog (Computer program language)LISP (Computer program language)