Essentials of Artificial Intelligence
- 1st Edition - November 13, 2012
- Latest edition
- Author: Matt Ginsberg
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 4 1 2 1 5 3 - 9
- 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
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 Defining Artificial Intelligence
- 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
- 2.1 Intelligent Action2.2 Search
- 2.2.1 Blind Search2.2.2 Heuristic Search2.2.3 Other Issues2.2.4 Search: Examples
- 2.3.1 Knowledge Representation: Examples
- 3.1 Breadth-First Search3.2 Depth-First Search3.3 Iterative Deepening3.4 Iterative Broadening3.5 Searching Graphs
- 3.5.1 Open and Closed Lists3.5.2 Dynamic Backtracking
- 4.1 Search as Function Maximization
- 4.1.1 Hill Climbing4.1.2 Simulated Annealing
- 4.2.1 Admissibility4.2.2 Examples
- 5.1 Assumptions5.2 Minimax
- 5.2.1 Quiescence and Singular Extensions5.2.2 The Horizon Effect
- 6.1 A Programming Analogy6.2 Syntax6.3 Semantics6.4 Soundness and Completeness6.5 how Hard Is Theorem Proving?6.6 Further Reading6.7 Exercises
- 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 Exercises
- 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 Exercises
- 9.1 Resolution Strategies9.2 Compile-Time and Run-Time Control9.3 The Role of Metalevel Reasoning in AI9.4 Runtime Control of Search
- 9.4.1 Lookahead9.4.2 The Cheapest-First Heuristic9.4.3 Dependency-Directed Backtracking and Backjumping
- 10.1 Definition10.2 Applications
- 10.2.1 Synthesis problems: Planning and Design10.2.2 Diagnosis10.2.3 Database Updates
- 11.1 Examples
- 11.1.1 Inheritance Hierarchies11.1.2 The Frame Problem11.1.3 Diagnosis
- 11.2.1 Extensions11.2.2 Multiple Extensions
- 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 Exercises
- 13.1 Introductory Examples
- 13.1.1 Frames13.1.2 Semantic Nets
- 13.2.1 Multiple Instances13.2.2 Nonunary Predicates
- 14.1 General-Purpose and Special-Purpose Planners14.2 Reasoning about Action14.3 Descriptions of Action
- 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
- 15.1 Discovery Learning15.2 Inductive Learning
- 15.2.1 PAC Learning15.2.2 Version Spaces15.2.3 Neural Networks15.2.4 ID3
- 16.1 Digitization16.2 Low-Level Processing
- 16.2.1 Noise Removal16.2.2 Feature Detection
- 16.4.1 The Waltz Algorithm16.4.2 The 2½-D Sketch
- 17.1 Signal Processing17.2 Syntax and Parsing17.3 Semantics and Meaning17.4 Pragmatics17.5 Natural Language Generation17.6 Further Reading17.7 Exercises
- 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 Exercises
- 19.1 Public Perception of AI19.2 Public Understanding of AI19.3 Applications of AI
- Edition: 1
- Latest edition
- Published: November 13, 2012
- Language: English
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