Essentials of Artificial Intelligence

1st Edition - April 1, 1993
Author: Matt Ginsberg
Language: English
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 1 3 9 6 8 - 7

Since its publication, Essentials of Artificial Intelligence has beenadopted at numerous universities and colleges offering introductory AIcourses at the graduate and undergrad… Read more

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Since its publication, Essentials of Artificial Intelligence has beenadopted at numerous universities and colleges offering introductory AIcourses at the graduate and undergraduate levels. Based on the author'scourse at Stanford University, the book is an integrated, cohesiveintroduction to the field. The author has a fresh, entertaining writingstyle that combines clear presentations with humor and AI anecdotes. At thesame time, as an active AI researcher, he presents the materialauthoritatively and with insight that reflects a contemporary, first handunderstanding of the field. Pedagogically designed, this book offers arange of exercises and examples.

Essentials of Artificial Intelligenceby Matt Ginsberg

Part I Introduction and Overview

1 Introduction: What is AI?

1.1.1 Intelligence1.1.2 Artifacts1.1.3 Construction

1.2.1 The Subfields of AI1.2.2 The Role of Examples in AI

2 Overview

2.2.1 Blind Search2.2.2 Heuristic Search2.2.3 Other Issues2.2.4 Search: Examples

2.3.1 Knowledge Representation: Examples

Part II Search

3 Blind Search

3.5.1 Open and Closed Lists3.5.2 Dynamic Backtracking

4 Heuristic Search

4.1.1 Hill Climbing4.1.2 Simulated Annealing

4.2.1 Admissibility4.2.2 Examples

5 Adversary Search

5.2.1 Quiescence and Singular Extensions5.2.2 The Horizon Effect

Part III Knowledge Representation: Logic6 Introduction to Knowledge Representation

6.1 A Programming Analogy6.2 Syntax6.3 Semantics6.4 Soundness and Completeness6.5 how Hard Is Theorem Proving?6.6 Further Reading6.7 Exercises7 Predicate Logic

7.1 Inference Using Modus Ponens7.2 Horn Databases7.3 The Resolution Rule7.4 Backward Chaining Using Resolution7.5 Normal Form7.6 Further Reading7.7 Exercises8 First-Order Logic

8.1 Databases with Quantifiers8.2 Unification8.3 Skolemizing Queries8.4 Finding the Most General Unifier8.5 Modus Ponens and Horn Databases8.6 Resolution and Normal Form8.7 Further Reading8.8 Exercises9 Putting Logic to Work: Control of Reasoning

9.4.1 Lookahead9.4.2 The Cheapest-First Heuristic9.4.3 Dependency-Directed Backtracking and Backjumping

Part IV Knowledge Representation: Other Techniques10 Assumption-Based Truth Maintenance

10.2.1 Synthesis problems: Planning and Design10.2.2 Diagnosis10.2.3 Database Updates

11 Nonmonotonic Reasoning

11.1.1 Inheritance Hierarchies11.1.2 The Frame Problem11.1.3 Diagnosis

11.2.1 Extensions11.2.2 Multiple Extensions

12 Probability

12.1 MYCIN and Certainty Factors12.2 Bayes' Rule and the Axioms of Probability12.3 Influence Diagrams12.4 Arguments For and Against Probability in AI12.5 Further Reading12.6 Exercises13 Putting Knowledge to Work: Frames and Semantic Nets

13.1.1 Frames13.1.2 Semantic Nets

13.2.1 Multiple Instances13.2.2 Nonunary Predicates

Part V AI Systems14 Planning

14.3.1 Nondeclarative Methods14.3.2 Monotonic Methods14.3.3 Nonmonotonic Methods

14.4.1 Hierarchical Planning14.4.2 Subgoal Ordering and Nonlinear Planning14.4.3 Subgoal Interaction and the Sussman Anomaly

15 Learning

15.2.1 PAC Learning15.2.2 Version Spaces15.2.3 Neural Networks15.2.4 ID3

16 Vision

16.2.1 Noise Removal16.2.2 Feature Detection

16.4.1 The Waltz Algorithm16.4.2 The 2½-D Sketch

17 Nature Language

17.1 Signal Processing17.2 Syntax and Parsing17.3 Semantics and Meaning17.4 Pragmatics17.5 Natural Language Generation17.6 Further Reading17.7 Exercises18 Expert Systems

18.1 Examples and History18.2 Advantages of Expert Systems18.3 CYC and Other VLKB Projects18.4 AI as an Experimental Discipline18.5 Further Reading18.6 Exercises19 Concluding Remarks

19.1 Public Perception of AI19.2 Public Understanding of AI19.3 Applications of AIBibliographyAuthor IndexSubject Index