Essentials of artificial intelligence
- Responsibility
- Matt Ginsberg.
- Imprint
- San Mateo, CA : Morgan Kaufmann Publishers, c1993.
- Physical description
- 430 p.
Access
Available online
SAL3 (off-campus storage)
Stacks
Request
| Call number | Status |
|---|---|
| Q335 .G55 1993 | |
| Q335 .G55 1993 |
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Creators/Contributors
- Author/Creator
- Ginsberg, Matthew L., 1955-
Contents/Summary
- Bibliography
- Includes bibliographical references and index.
- Contents
-
- 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 21/2-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.
- (source: Nielsen Book Data)9781558602212 20160528
- Publisher's Summary
- Since its publication, Essentials of Artificial Intelligence has been adopted at numerous universities and colleges offering introductory AI courses at the graduate and undergraduate levels. Based on the author's course at Stanford University, the book is an integrated, cohesive introduction to the field. The author has a fresh, entertaining writing style that combines clear presentations with humor and AI anecdotes. At the same time, as an active AI researcher, he presents the material authoritatively and with insight that reflects a contemporary, first hand understanding of the field. Pedagogically designed, this book offers a range of exercises and examples.
(source: Nielsen Book Data)9781558602212 20160528
Subjects
- Subject
- Artificial intelligence.
Bibliographic information
- Publication date
- 1993
- ISBN
- 1558602216
- 9781558602212
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