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Essentials of Artificial Intelligence

Essentials of Artificial Intelligence
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10022728 Access this eBook online Ebook for Applied Information Technology   GUtech Library . . Available .  
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ISBN 9780323139687
9781558602212
Author Ginsberg, Matt
Title Essentials of Artificial Intelligence
Description 1 online resource (445 pages)
Contents Front Cover -- Essentials of Artificial Intelligence -- Copyright Page -- Table of Contents -- Acknowledgement -- Preface -- PART I: INTRODUCTION AND OVERVIEW -- Chapter 1. Introduction: What Is AI? -- 1.1 Defining Artificial Intelligence -- 1.2 What AI Is About -- 1.3 What AI Is Like -- 1.4 Further Reading -- 1.5 Exercises -- Chapter 2. Overview -- 2.1 Intelligent Action -- 2.2 Search -- 2.3 Knowledge Representation -- 2.4 Applications: Examples -- 2.5 Further Reading -- 2.6 Exercises -- PART II: SEARCH -- Chapter 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 -- 3.6 Further Reading -- 3.7 Exercises -- Chapter 4. Heuristic Search -- 4.1 Search as Function Maximization -- 4.2 A* -- 4.3 Extensions and IDA* -- 4.4 Further Reading -- 4.5 Exercises -- Chapter 5. Adversary Search -- 5.1 Assumptions -- 5.2 Minimax -- 5.3 a-p Search -- 5.4 Further Reading -- 5.5 Exercises -- PART III: KNOWLEDGE REPRESENTATION : LOGIC -- Chapter 6. Introduction to Knowledge Representation and Logic -- 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 -- Chapter 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 -- Chapter 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 -- Chapter 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 -- 9.5 Declarative Control of Search -- 9.6 Further Reading -- 9.7 Exercises -- PART IV: KNOWLEDGERE PRESENTATION: OTHER TECHNIQUES -- Chapter 10. Assumption-Based Truth Maintenance -- 10.1 Definition -- 10.2 Applications -- 10.3 Implementation -- 10.4 Further Reading -- 10.5 Exercises -- Chapter 11. Nonmonotonic Reasoning -- 11.1 Examples -- 11.2 Definition -- 11.3 Computational Problems -- 11.4 Final Remarks -- 11.5 Further Reading -- 11.6 Exercises -- Chapter 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 -- Chapter 13. Putting Knowledge to Work: Frames and Semantic Nets -- 13.1 Introductory Examples -- 13.2 Extensions -- 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 -- Chapter 14. Planning -- 14.1 General-Purpose and Special-Purpose Planners -- 14.2 Reasoning about Action -- 14.3 Descriptions of Action -- 14.4 Search in Planning -- 14.5 Implementing a Planner -- 14.6 Further Reading -- 14.7 Exercises -- Chapter 15. Learning -- 15.1 Discovery Learning -- 15.2 Inductive Learning -- 15.3 Explanation-Based Learning -- 15.4 Further Reading -- 15.5 Exercises -- Chapter 16. Vision -- 16.1 Digitization -- 16.2 Low-Level Processing -- 16.3 Segmentation and the Hough Transform -- 16.4 Recovering 3-D Information -- 16.5 Active Vision -- 16.6 Object and Scene Recognition -- 16.7 Further Reading -- 16.8 Exercises -- Chapter 17. Natural 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 -- Chapter 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 -- Chapter 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.
Subject Artificial intelligence
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