Essentials of Artificial Intelligence
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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.
Table of Contents
- Essentials of Artificial Intelligence
by Matt Ginsberg- Part I Introduction and Overview
- 1 Introduction: What is AI?
- 1.1 Defining Artificial Intelligence
- 1.1.1 Intelligence
1.1.2 Artifacts
1.1.3 Construction
1.2 What AI is About- 1.2.1 The Subfields of AI
1.2.2 The Role of Examples in AI
1.3 What AI Is Like
1.4 Further Reading
1.5 Exercises
2 Overview- 2.1 Intelligent Action
2.2 Search- 2.2.1 Blind Search
2.2.2 Heuristic Search
2.2.3 Other Issues
2.2.4 Search: Examples
2.3 Knowledge Representation- 2.3.1 Knowledge Representation: Examples
2.4 Applications: Examples
2.5 Further Reading
2.6 Exercises
Part II Search
3 Blind Search- 3.1 Breadth-First Search
3.2 Depth-First Search
3.3 Iterative Deepening
3.4 Iterative Broadening
3.5 Searching Graphs- 3.5.1 Open and Closed Lists
3.5.2 Dynamic Backtracking
3.6 Further Reading
3.7 Exercises
4 Heuristic Search- 4.1 Search as Function Maximization
- 4.1.1 Hill Climbing
4.1.2 Simulated Annealing
4.2 A*- 4.2.1 Admissibility
4.2.2 Examples
4.3 Extensions and IDA*
4.4 Further Reading
4.5 Exercises
5 Adversary Search- 5.1 Assumptions
5.2 Minimax- 5.2.1 Quiescence and Singular Extensions
5.2.2 The Horizon Effect
5.3 ((( Search
5.4 Further Reading
5.5 Exercises
Part III Knowledge Representation: Logic
6 Introduction to Knowledge Representation- 6.1 A Programming Analogy
6.2 Syntax
6.3 Semantics
6.4 Soundness and Completeness
6.5 how Hard Is Theorem Proving?
6.6 Further Reading
6.7 Exercises
7 Predicate Logic- 7.1 Inference Using Modus Ponens
7.2 Horn Databases
7.3 The Resolution Rule
7.4 Backward Chaining Using Resolution
7.5 Normal Form
7.6 Further Reading
7.7 Exercises
8 First-Order Logic- 8.1 Databases with Quantifiers
8.2 Unification
8.3 Skolemizing Queries
8.4 Finding the Most General Unifier
8.5 Modus Ponens and Horn Databases
8.6 Resolution and Normal Form
8.7 Further Reading
8.8 Exercises
9 Putting Logic to Work: Control of Reasoning- 9.1 Resolution Strategies
9.2 Compile-Time and Run-Time Control
9.3 The Role of Metalevel Reasoning in AI
9.4 Runtime Control of Search- 9.4.1 Lookahead
9.4.2 The Cheapest-First Heuristic
9.4.3 Dependency-Directed Backtracking and Backjumping
9.5 Declarative Control of Search
9.6 Further Reading
9.7 Exercises
Part IV Knowledge Representation: Other Techniques
10 Assumption-Based Truth Maintenance
- 10.1 Definition
10.2 Applications
- 10.2.1 Synthesis problems: Planning and Design
10.2.2 Diagnosis
10.2.3 Database Updates
10.3 Implementation
10.4 Further Reading
10.5 Exercises
11 Nonmonotonic Reasoning
- 11.1 Examples
- 11.1.1 Inheritance Hierarchies
11.1.2 The Frame Problem
11.1.3 Diagnosis
11.2 Definition
- 11.2.1 Extensions
11.2.2 Multiple Extensions
11.3 Computational Problems
11.4 Final Remarks
11.5 Further Reading
11.6 Exercises
12 Probability
- 12.1 MYCIN and Certainty Factors
12.2 Bayes' Rule and the Axioms of Probability
12.3 Influence Diagrams
12.4 Arguments For and Against Probability in AI
12.5 Further Reading
12.6 Exercises
13 Putting Knowledge to Work: Frames and Semantic Nets
- 13.1 Introductory Examples
- 13.1.1 Frames
13.1.2 Semantic Nets
13.2 Extensions
- 13.2.1 Multiple Instances
13.2.2 Nonunary Predicates
13.3 Inference in Monotonic Frame Systems
13.4 Inference in Nonmonotonic Frame Systems
13.5 Further Reading
13.6 Exercises
Part V AI Systems
14 Planning
- 14.1 General-Purpose and Special-Purpose Planners
14.2 Reasoning about Action
14.3 Descriptions of Action
- 14.3.1 Nondeclarative Methods
14.3.2 Monotonic Methods
14.3.3 Nonmonotonic Methods
14.4 Search in Planning
- 14.4.1 Hierarchical Planning
14.4.2 Subgoal Ordering and Nonlinear Planning
14.4.3 Subgoal Interaction and the Sussman Anomaly
14.5 Implementing a Planner
14.6 Further Reading
14.7 Exercises
15 Learning
- 15.1 Discovery Learning
15.2 Inductive Learning
- 15.2.1 PAC Learning
15.2.2 Version Spaces
15.2.3 Neural Networks
15.2.4 ID3
15.3 Explanation-Based Learning
15.4 Further Reading
15.5 Exercises
16 Vision
- 16.1 Digitization
16.2 Low-Level Processing
- 16.2.1 Noise Removal
16.2.2 Feature Detection
16.3 Segmentation and the Hough Transform
16.4 Recovering 3-D Information
- 16.4.1 The Waltz Algorithm
16.4.2 The 2½-D Sketch
16.5 Active Vision
16.6 Object and Scene Recognition
16.7 Further Reading
16.8 Exercises
17 Nature Language
- 17.1 Signal Processing
17.2 Syntax and Parsing
17.3 Semantics and Meaning
17.4 Pragmatics
17.5 Natural Language Generation
17.6 Further Reading
17.7 Exercises
18 Expert Systems
- 18.1 Examples and History
18.2 Advantages of Expert Systems
18.3 CYC and Other VLKB Projects
18.4 AI as an Experimental Discipline
18.5 Further Reading
18.6 Exercises
19 Concluding Remarks
- 19.1 Public Perception of AI
19.2 Public Understanding of AI
19.3 Applications of AI
Bibliography
Author Index
Subject Index
Product details
- No. of pages: 430
- Language: English
- Copyright: © Morgan Kaufmann 1993
- Published: April 1, 1993
- Imprint: Morgan Kaufmann
- eBook ISBN: 9780323139687
About the Author
Matt Ginsberg
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