| Description |
1 online resource (467 pages) |
| Series |
Chapman and Hall/CRC Artificial Intelligence and Robotics Ser |
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Chapman and Hall/CRC Artificial Intelligence and Robotics Ser
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| Contents |
Intro; Title Page; Copyright Page; Table of Contents; Preface; About the Authors; 1 Introduction to Artificial Intelligence; 1.1 History of Artificial Intelligence; 1.1.1 What Is Artificial Intelligence?; 1.1.2 Emergence of AI; 1.1.3 Cognitive Science and AI; 1.1.4 Logical Approach to AI; 1.1.5 Knowledge-Based Systems; 1.1.6 Probabilistic Approach to AI; 1.1.7 Evolutionary Computation and Swarm Intelligence; 1.1.8 Neural Networks & Deep Learning; 1.1.9 A Return to Creating HAL; 1.2 Outline of This Book; PART I Logical Intelligence; 2 Propositional logic; 2.1 Basics of propositional logic |
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2.1.1 Syntax2.1.2 Semantics; 2.1.3 Tautologies and logical implication; 2.1.4 Logical arguments; 2.1.5 Derivation systems; 2.2 Resolution; 2.2.1 Normal forms; 2.2.2 Derivations using resolution; 2.2.3 Resolution algorithm; 2.3 Artificial intelligence applications; 2.3.1 Knowledge-based systems; 2.3.2 Wumpus world; 2.4 Discussion and further reading; 3 First-order logic; 3.1 Basics of first-order logic; 3.1.1 Syntax; 3.1.2 Semantics; 3.1.3 Validity and Logical Implication; 3.1.4 Derivation Systems; 3.1.5 Modus Ponens for First-Order Logic; 3.2 Artificial Intelligence Applications |
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3.2.1 Wumpus World Revisited3.2.2 Planning; 3.3 Discussion and Further Reading; 4 Certain knowledge representation; 4.1 Taxonomic Knowledge; 4.1.1 Semantic Nets; 4.1.2 Model of Human Organization of Knowledge; 4.2 Frames; 4.2.1 Frame Data Structure; 4.2.2 Planning a Trip Using Frames; 4.3 Nonmonotonic Logic; 4.3.1 Circumscription; 4.3.2 Default Logic; 4.3.3 Difficulties; 4.4 Discussion and Further Reading; 5 Learning deterministic models; 5.1 Supervised Learning; 5.2 Regression; 5.2.1 Simple Linear Regression; 5.2.2 Multiple Linear Regression; 5.2.3 Overfitting and Cross Validation |
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5.3 Parameter Estimation5.3.1 Estimating the Parameters for Simple Linear Regression; 5.3.2 Gradient Descent; 5.3.3 Logistic Regression and Gradient Descent; 5.3.4 Stochastic Gradient Descent; 5.4 Learning a Decision Tree; 5.4.1 Information Theory; 5.4.2 Information Gain and the ID3 Algorithm; 5.4.3 Overfitting; PART II Probabilistic Intelligence; 6 Probability; 6.1 Probability Basics; 6.1.1 Probability Spaces; 6.1.2 Conditional Probability and Independence; 6.1.3 Bayesâ#x80;#x99; Theorem; 6.2 Random Variables; 6.2.1 Probability Distributions of Random Variables |
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6.2.2 Independence of Random Variables6.3 Meaning of Probability; 6.3.1 Relative Frequency Approach to Probability; 6.3.2 Subjective Approach to Probability; 6.4 Random Variables in Applications; 6.5 Probability in the Wumpus World; 7 Uncertain Knowledge Representation; 7.1 Intuitive Introduction to Bayesian Networks; 7.2 Properties of Bayesian Networks; 7.2.1 Definition of a Bayesian Network; 7.2.2 Representation of a Bayesian Network; 7.3 Causal Networks as Bayesian Networks; 7.3.1 Causality; 7.3.2 Causality and the Markov Condition; 7.3.3 Markov Condition without Causality |
| Summary |
The first edition of this popular textbook, Contemporary Artificial Intelligence, provided an accessible and student friendly introduction to AI. This fully revised and expanded update, Artificial Intelligence: With an Introduction to Machine Learning, Second Edition, retains the same accessibility and problem-solving approach, while providing new material and methods. The book is divided into five sections that focus on the most useful techniques that have emerged from AI. The first section of the book covers logic-based methods, while the second section focuses on probability-based methods. Emergent intelligence is featured in the third section and explores evolutionary computation and methods based on swarm intelligence. The newest section comes next and provides a detailed overview of neural networks and deep learning. The final section of the book focuses on natural language understanding. Suitable for undergraduate and beginning graduate students, this class-tested textbook provides students and other readers with key AI methods and algorithms for solving challenging problems involving systems that behave intelligently in specialized domains such as medical and software diagnostics, financial decision making, speech and text recognition, genetic analysis, and more |
| Notes |
7.4 Inference in Bayesian Networks |
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Print version record |
| Subject |
Artificial intelligence.
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Artificial Intelligence
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artificial intelligence.
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COMPUTERS -- Software Development & Engineering -- Systems Analysis & Design.
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TECHNOLOGY & ENGINEERING -- Electronics -- General.
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algorithms of AI.
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artificial intelligence problems.
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artificial intelligence textbook.
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emergent intelligence.
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evolutionary computation.
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importance of AI techniques in computer science.
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logic-based methods.
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natural language understanding.
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probabilistic learning models.
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probability-based methods.
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strong AI methods.
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swarm intelligence.
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Artificial intelligence
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| Form |
Electronic book
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| Author |
Jiang, Xia
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| ISBN |
9781351384384 |
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1351384384 |
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9781351384391 |
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1351384392 |
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